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| author | Huon Wilson <dbau.pp+github@gmail.com> | 2014-03-02 11:23:04 +1100 |
|---|---|---|
| committer | Huon Wilson <dbau.pp+github@gmail.com> | 2014-03-12 11:31:05 +1100 |
| commit | 6fa4bbeed425ae99d15322fbaa05d1abeae6547f (patch) | |
| tree | 17f9edde3db9c624e476ac2f6c585792ddb1890b /src/libstd | |
| parent | 74bfa7108a62c053fdeae2bb093f8035e19e2ef2 (diff) | |
| download | rust-6fa4bbeed425ae99d15322fbaa05d1abeae6547f.tar.gz rust-6fa4bbeed425ae99d15322fbaa05d1abeae6547f.zip | |
std: Move rand to librand.
This functionality is not super-core and so doesn't need to be included in std. It's possible that std may need rand (it does a little bit now, for io::test) in which case the functionality required could be moved to a secret hidden module and reexposed by librand. Unfortunately, using #[deprecated] here is hard: there's too much to mock to make it feasible, since we have to ensure that programs still typecheck to reach the linting phase.
Diffstat (limited to 'src/libstd')
| -rw-r--r-- | src/libstd/lib.rs | 4 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/exponential.rs | 142 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/gamma.rs | 408 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/mod.rs | 357 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/normal.rs | 210 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/range.rs | 234 | ||||
| -rw-r--r-- | src/libstd/rand/distributions/ziggurat_tables.rs | 280 | ||||
| -rw-r--r-- | src/libstd/rand/isaac.rs | 535 | ||||
| -rw-r--r-- | src/libstd/rand/mod.rs | 906 | ||||
| -rw-r--r-- | src/libstd/rand/os.rs | 188 | ||||
| -rw-r--r-- | src/libstd/rand/rand_impls.rs | 278 | ||||
| -rw-r--r-- | src/libstd/rand/reader.rs | 124 | ||||
| -rw-r--r-- | src/libstd/rand/reseeding.rs | 224 |
13 files changed, 2 insertions, 3888 deletions
diff --git a/src/libstd/lib.rs b/src/libstd/lib.rs index 4d3d1641bd0..a873eccfb03 100644 --- a/src/libstd/lib.rs +++ b/src/libstd/lib.rs @@ -73,7 +73,8 @@ #[cfg(test)] extern crate native; #[cfg(test)] extern crate green; -// Make extra accessible for benchmarking +// Make extra and rand accessible for benchmarking/testcases +#[cfg(test)] extern crate rand; #[cfg(test)] extern crate extra = "extra"; // Make std testable by not duplicating lang items. See #2912 @@ -173,7 +174,6 @@ pub mod c_str; pub mod os; pub mod io; pub mod path; -pub mod rand; pub mod cast; pub mod fmt; pub mod cleanup; diff --git a/src/libstd/rand/distributions/exponential.rs b/src/libstd/rand/distributions/exponential.rs deleted file mode 100644 index 2fa9cf8bd48..00000000000 --- a/src/libstd/rand/distributions/exponential.rs +++ /dev/null @@ -1,142 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The exponential distribution. - -use num::Float; -use rand::{Rng, Rand}; -use rand::distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample}; - -/// A wrapper around an `f64` to generate Exp(1) random numbers. -/// -/// See `Exp` for the general exponential distribution.Note that this - // has to be unwrapped before use as an `f64` (using either -/// `*` or `cast::transmute` is safe). -/// -/// Implemented via the ZIGNOR variant[1] of the Ziggurat method. The -/// exact description in the paper was adjusted to use tables for the -/// exponential distribution rather than normal. -/// -/// [1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random -/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield -/// College, Oxford -pub struct Exp1(f64); - -// This could be done via `-rng.gen::<f64>().ln()` but that is slower. -impl Rand for Exp1 { - #[inline] - fn rand<R:Rng>(rng: &mut R) -> Exp1 { - #[inline] - fn pdf(x: f64) -> f64 { - (-x).exp() - } - #[inline] - fn zero_case<R:Rng>(rng: &mut R, _u: f64) -> f64 { - ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln() - } - - Exp1(ziggurat(rng, false, - &ziggurat_tables::ZIG_EXP_X, - &ziggurat_tables::ZIG_EXP_F, - pdf, zero_case)) - } -} - -/// The exponential distribution `Exp(lambda)`. -/// -/// This distribution has density function: `f(x) = lambda * -/// exp(-lambda * x)` for `x > 0`. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{Exp, IndependentSample}; -/// -/// let exp = Exp::new(2.0); -/// let v = exp.ind_sample(&mut rand::task_rng()); -/// println!("{} is from a Exp(2) distribution", v); -/// ``` -pub struct Exp { - /// `lambda` stored as `1/lambda`, since this is what we scale by. - priv lambda_inverse: f64 -} - -impl Exp { - /// Construct a new `Exp` with the given shape parameter - /// `lambda`. Fails if `lambda <= 0`. - pub fn new(lambda: f64) -> Exp { - assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0"); - Exp { lambda_inverse: 1.0 / lambda } - } -} - -impl Sample<f64> for Exp { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for Exp { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let Exp1(n) = rng.gen::<Exp1>(); - n * self.lambda_inverse - } -} - -#[cfg(test)] -mod test { - use rand::distributions::*; - use prelude::*; - use rand::*; - use super::*; - - #[test] - fn test_exp() { - let mut exp = Exp::new(10.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - assert!(exp.sample(&mut rng) >= 0.0); - assert!(exp.ind_sample(&mut rng) >= 0.0); - } - } - #[test] - #[should_fail] - fn test_exp_invalid_lambda_zero() { - Exp::new(0.0); - } - #[test] - #[should_fail] - fn test_exp_invalid_lambda_neg() { - Exp::new(-10.0); - } -} - -#[cfg(test)] -mod bench { - extern crate test; - use self::test::BenchHarness; - use mem::size_of; - use prelude::*; - use rand::{XorShiftRng, RAND_BENCH_N}; - use super::*; - use rand::distributions::*; - - #[bench] - fn rand_exp(bh: &mut BenchHarness) { - let mut rng = XorShiftRng::new(); - let mut exp = Exp::new(2.71828 * 3.14159); - - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - exp.sample(&mut rng); - } - }); - bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N; - } -} diff --git a/src/libstd/rand/distributions/gamma.rs b/src/libstd/rand/distributions/gamma.rs deleted file mode 100644 index b9702ccd48d..00000000000 --- a/src/libstd/rand/distributions/gamma.rs +++ /dev/null @@ -1,408 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The Gamma and derived distributions. - -use num::Float; -use num; -use rand::{Rng, Open01}; -use super::normal::StandardNormal; -use super::{IndependentSample, Sample, Exp}; - -/// The Gamma distribution `Gamma(shape, scale)` distribution. -/// -/// The density function of this distribution is -/// -/// ```ignore -/// f(x) = x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k) -/// ``` -/// -/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the -/// scale and both `k` and `θ` are strictly positive. -/// -/// The algorithm used is that described by Marsaglia & Tsang 2000[1], -/// falling back to directly sampling from an Exponential for `shape -/// == 1`, and using the boosting technique described in [1] for -/// `shape < 1`. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{IndependentSample, Gamma}; -/// -/// let gamma = Gamma::new(2.0, 5.0); -/// let v = gamma.ind_sample(&mut rand::task_rng()); -/// println!("{} is from a Gamma(2, 5) distribution", v); -/// ``` -/// -/// [1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method -/// for Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3 -/// (September 2000), -/// 363-372. DOI:[10.1145/358407.358414](http://doi.acm.org/10.1145/358407.358414) -pub enum Gamma { - priv Large(GammaLargeShape), - priv One(Exp), - priv Small(GammaSmallShape) -} - -// These two helpers could be made public, but saving the -// match-on-Gamma-enum branch from using them directly (e.g. if one -// knows that the shape is always > 1) doesn't appear to be much -// faster. - -/// Gamma distribution where the shape parameter is less than 1. -/// -/// Note, samples from this require a compulsory floating-point `pow` -/// call, which makes it significantly slower than sampling from a -/// gamma distribution where the shape parameter is greater than or -/// equal to 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -struct GammaSmallShape { - inv_shape: f64, - large_shape: GammaLargeShape -} - -/// Gamma distribution where the shape parameter is larger than 1. -/// -/// See `Gamma` for sampling from a Gamma distribution with general -/// shape parameters. -struct GammaLargeShape { - shape: f64, - scale: f64, - c: f64, - d: f64 -} - -impl Gamma { - /// Construct an object representing the `Gamma(shape, scale)` - /// distribution. - /// - /// Fails if `shape <= 0` or `scale <= 0`. - pub fn new(shape: f64, scale: f64) -> Gamma { - assert!(shape > 0.0, "Gamma::new called with shape <= 0"); - assert!(scale > 0.0, "Gamma::new called with scale <= 0"); - - match shape { - 1.0 => One(Exp::new(1.0 / scale)), - 0.0 .. 1.0 => Small(GammaSmallShape::new_raw(shape, scale)), - _ => Large(GammaLargeShape::new_raw(shape, scale)) - } - } -} - -impl GammaSmallShape { - fn new_raw(shape: f64, scale: f64) -> GammaSmallShape { - GammaSmallShape { - inv_shape: 1. / shape, - large_shape: GammaLargeShape::new_raw(shape + 1.0, scale) - } - } -} - -impl GammaLargeShape { - fn new_raw(shape: f64, scale: f64) -> GammaLargeShape { - let d = shape - 1. / 3.; - GammaLargeShape { - shape: shape, - scale: scale, - c: 1. / num::sqrt(9. * d), - d: d - } - } -} - -impl Sample<f64> for Gamma { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl Sample<f64> for GammaSmallShape { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl Sample<f64> for GammaLargeShape { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} - -impl IndependentSample<f64> for Gamma { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - match *self { - Small(ref g) => g.ind_sample(rng), - One(ref g) => g.ind_sample(rng), - Large(ref g) => g.ind_sample(rng), - } - } -} -impl IndependentSample<f64> for GammaSmallShape { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let Open01(u) = rng.gen::<Open01<f64>>(); - - self.large_shape.ind_sample(rng) * num::powf(u, self.inv_shape) - } -} -impl IndependentSample<f64> for GammaLargeShape { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - loop { - let StandardNormal(x) = rng.gen::<StandardNormal>(); - let v_cbrt = 1.0 + self.c * x; - if v_cbrt <= 0.0 { // a^3 <= 0 iff a <= 0 - continue - } - - let v = v_cbrt * v_cbrt * v_cbrt; - let Open01(u) = rng.gen::<Open01<f64>>(); - - let x_sqr = x * x; - if u < 1.0 - 0.0331 * x_sqr * x_sqr || - num::ln(u) < 0.5 * x_sqr + self.d * (1.0 - v + num::ln(v)) { - return self.d * v * self.scale - } - } - } -} - -/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of -/// freedom. -/// -/// For `k > 0` integral, this distribution is the sum of the squares -/// of `k` independent standard normal random variables. For other -/// `k`, this uses the equivalent characterisation `χ²(k) = Gamma(k/2, -/// 2)`. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{ChiSquared, IndependentSample}; -/// -/// let chi = ChiSquared::new(11.0); -/// let v = chi.ind_sample(&mut rand::task_rng()); -/// println!("{} is from a χ²(11) distribution", v) -/// ``` -pub enum ChiSquared { - // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1, - // e.g. when alpha = 1/2 as it would be for this case, so special- - // casing and using the definition of N(0,1)^2 is faster. - priv DoFExactlyOne, - priv DoFAnythingElse(Gamma) -} - -impl ChiSquared { - /// Create a new chi-squared distribution with degrees-of-freedom - /// `k`. Fails if `k < 0`. - pub fn new(k: f64) -> ChiSquared { - if k == 1.0 { - DoFExactlyOne - } else { - assert!(k > 0.0, "ChiSquared::new called with `k` < 0"); - DoFAnythingElse(Gamma::new(0.5 * k, 2.0)) - } - } -} -impl Sample<f64> for ChiSquared { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for ChiSquared { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - match *self { - DoFExactlyOne => { - // k == 1 => N(0,1)^2 - let StandardNormal(norm) = rng.gen::<StandardNormal>(); - norm * norm - } - DoFAnythingElse(ref g) => g.ind_sample(rng) - } - } -} - -/// The Fisher F distribution `F(m, n)`. -/// -/// This distribution is equivalent to the ratio of two normalised -/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) / -/// (χ²(n)/n)`. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{FisherF, IndependentSample}; -/// -/// let f = FisherF::new(2.0, 32.0); -/// let v = f.ind_sample(&mut rand::task_rng()); -/// println!("{} is from an F(2, 32) distribution", v) -/// ``` -pub struct FisherF { - priv numer: ChiSquared, - priv denom: ChiSquared, - // denom_dof / numer_dof so that this can just be a straight - // multiplication, rather than a division. - priv dof_ratio: f64, -} - -impl FisherF { - /// Create a new `FisherF` distribution, with the given - /// parameter. Fails if either `m` or `n` are not positive. - pub fn new(m: f64, n: f64) -> FisherF { - assert!(m > 0.0, "FisherF::new called with `m < 0`"); - assert!(n > 0.0, "FisherF::new called with `n < 0`"); - - FisherF { - numer: ChiSquared::new(m), - denom: ChiSquared::new(n), - dof_ratio: n / m - } - } -} -impl Sample<f64> for FisherF { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for FisherF { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - self.numer.ind_sample(rng) / self.denom.ind_sample(rng) * self.dof_ratio - } -} - -/// The Student t distribution, `t(nu)`, where `nu` is the degrees of -/// freedom. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{StudentT, IndependentSample}; -/// -/// let t = StudentT::new(11.0); -/// let v = t.ind_sample(&mut rand::task_rng()); -/// println!("{} is from a t(11) distribution", v) -/// ``` -pub struct StudentT { - priv chi: ChiSquared, - priv dof: f64 -} - -impl StudentT { - /// Create a new Student t distribution with `n` degrees of - /// freedom. Fails if `n <= 0`. - pub fn new(n: f64) -> StudentT { - assert!(n > 0.0, "StudentT::new called with `n <= 0`"); - StudentT { - chi: ChiSquared::new(n), - dof: n - } - } -} -impl Sample<f64> for StudentT { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for StudentT { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let StandardNormal(norm) = rng.gen::<StandardNormal>(); - norm * (self.dof / self.chi.ind_sample(rng)).sqrt() - } -} - -#[cfg(test)] -mod test { - use rand::distributions::*; - use prelude::*; - use rand::*; - use super::*; - - #[test] - fn test_chi_squared_one() { - let mut chi = ChiSquared::new(1.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - fn test_chi_squared_small() { - let mut chi = ChiSquared::new(0.5); - let mut rng = task_rng(); - for _ in range(0, 1000) { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - fn test_chi_squared_large() { - let mut chi = ChiSquared::new(30.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - chi.sample(&mut rng); - chi.ind_sample(&mut rng); - } - } - #[test] - #[should_fail] - fn test_log_normal_invalid_dof() { - ChiSquared::new(-1.0); - } - - #[test] - fn test_f() { - let mut f = FisherF::new(2.0, 32.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - f.sample(&mut rng); - f.ind_sample(&mut rng); - } - } - - #[test] - fn test_t() { - let mut t = StudentT::new(11.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - t.sample(&mut rng); - t.ind_sample(&mut rng); - } - } -} - -#[cfg(test)] -mod bench { - extern crate test; - use self::test::BenchHarness; - use mem::size_of; - use prelude::*; - use rand::distributions::IndependentSample; - use rand::{StdRng, RAND_BENCH_N}; - use super::*; - - - #[bench] - fn bench_gamma_large_shape(bh: &mut BenchHarness) { - let gamma = Gamma::new(10., 1.0); - let mut rng = StdRng::new(); - - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - gamma.ind_sample(&mut rng); - } - }); - bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N; - } - - #[bench] - fn bench_gamma_small_shape(bh: &mut BenchHarness) { - let gamma = Gamma::new(0.1, 1.0); - let mut rng = StdRng::new(); - - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - gamma.ind_sample(&mut rng); - } - }); - bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N; - } -} diff --git a/src/libstd/rand/distributions/mod.rs b/src/libstd/rand/distributions/mod.rs deleted file mode 100644 index 41a106ec887..00000000000 --- a/src/libstd/rand/distributions/mod.rs +++ /dev/null @@ -1,357 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -/*! -Sampling from random distributions. - -This is a generalization of `Rand` to allow parameters to control the -exact properties of the generated values, e.g. the mean and standard -deviation of a normal distribution. The `Sample` trait is the most -general, and allows for generating values that change some state -internally. The `IndependentSample` trait is for generating values -that do not need to record state. - -*/ - -use container::Container; -use iter::{range, Iterator}; -use option::{Some, None}; -use num; -use num::CheckedAdd; -use rand::{Rng, Rand}; -use clone::Clone; -use vec::MutableVector; - -pub use self::range::Range; -pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT}; -pub use self::normal::{Normal, LogNormal}; -pub use self::exponential::Exp; - -pub mod range; -pub mod gamma; -pub mod normal; -pub mod exponential; - -/// Types that can be used to create a random instance of `Support`. -pub trait Sample<Support> { - /// Generate a random value of `Support`, using `rng` as the - /// source of randomness. - fn sample<R: Rng>(&mut self, rng: &mut R) -> Support; -} - -/// `Sample`s that do not require keeping track of state. -/// -/// Since no state is recorded, each sample is (statistically) -/// independent of all others, assuming the `Rng` used has this -/// property. -// FIXME maybe having this separate is overkill (the only reason is to -// take &self rather than &mut self)? or maybe this should be the -// trait called `Sample` and the other should be `DependentSample`. -pub trait IndependentSample<Support>: Sample<Support> { - /// Generate a random value. - fn ind_sample<R: Rng>(&self, &mut R) -> Support; -} - -/// A wrapper for generating types that implement `Rand` via the -/// `Sample` & `IndependentSample` traits. -pub struct RandSample<Sup>; - -impl<Sup: Rand> Sample<Sup> for RandSample<Sup> { - fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) } -} - -impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup { - rng.gen() - } -} - -/// A value with a particular weight for use with `WeightedChoice`. -pub struct Weighted<T> { - /// The numerical weight of this item - weight: uint, - /// The actual item which is being weighted - item: T, -} - -/// A distribution that selects from a finite collection of weighted items. -/// -/// Each item has an associated weight that influences how likely it -/// is to be chosen: higher weight is more likely. -/// -/// The `Clone` restriction is a limitation of the `Sample` and -/// `IndependentSample` traits. Note that `&T` is (cheaply) `Clone` for -/// all `T`, as is `uint`, so one can store references or indices into -/// another vector. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{Weighted, WeightedChoice, IndependentSample}; -/// -/// let wc = WeightedChoice::new(~[Weighted { weight: 2, item: 'a' }, -/// Weighted { weight: 4, item: 'b' }, -/// Weighted { weight: 1, item: 'c' }]); -/// let mut rng = rand::task_rng(); -/// for _ in range(0, 16) { -/// // on average prints 'a' 4 times, 'b' 8 and 'c' twice. -/// println!("{}", wc.ind_sample(&mut rng)); -/// } -/// ``` -pub struct WeightedChoice<T> { - priv items: ~[Weighted<T>], - priv weight_range: Range<uint> -} - -impl<T: Clone> WeightedChoice<T> { - /// Create a new `WeightedChoice`. - /// - /// Fails if: - /// - `v` is empty - /// - the total weight is 0 - /// - the total weight is larger than a `uint` can contain. - pub fn new(mut items: ~[Weighted<T>]) -> WeightedChoice<T> { - // strictly speaking, this is subsumed by the total weight == 0 case - assert!(!items.is_empty(), "WeightedChoice::new called with no items"); - - let mut running_total = 0u; - - // we convert the list from individual weights to cumulative - // weights so we can binary search. This *could* drop elements - // with weight == 0 as an optimisation. - for item in items.mut_iter() { - running_total = running_total.checked_add(&item.weight) - .expect("WeightedChoice::new called with a total weight larger \ - than a uint can contain"); - - item.weight = running_total; - } - assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0"); - - WeightedChoice { - items: items, - // we're likely to be generating numbers in this range - // relatively often, so might as well cache it - weight_range: Range::new(0, running_total) - } - } -} - -impl<T: Clone> Sample<T> for WeightedChoice<T> { - fn sample<R: Rng>(&mut self, rng: &mut R) -> T { self.ind_sample(rng) } -} - -impl<T: Clone> IndependentSample<T> for WeightedChoice<T> { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> T { - // we want to find the first element that has cumulative - // weight > sample_weight, which we do by binary since the - // cumulative weights of self.items are sorted. - - // choose a weight in [0, total_weight) - let sample_weight = self.weight_range.ind_sample(rng); - - // short circuit when it's the first item - if sample_weight < self.items[0].weight { - return self.items[0].item.clone(); - } - - let mut idx = 0; - let mut modifier = self.items.len(); - - // now we know that every possibility has an element to the - // left, so we can just search for the last element that has - // cumulative weight <= sample_weight, then the next one will - // be "it". (Note that this greatest element will never be the - // last element of the vector, since sample_weight is chosen - // in [0, total_weight) and the cumulative weight of the last - // one is exactly the total weight.) - while modifier > 1 { - let i = idx + modifier / 2; - if self.items[i].weight <= sample_weight { - // we're small, so look to the right, but allow this - // exact element still. - idx = i; - // we need the `/ 2` to round up otherwise we'll drop - // the trailing elements when `modifier` is odd. - modifier += 1; - } else { - // otherwise we're too big, so go left. (i.e. do - // nothing) - } - modifier /= 2; - } - return self.items[idx + 1].item.clone(); - } -} - -mod ziggurat_tables; - -/// Sample a random number using the Ziggurat method (specifically the -/// ZIGNOR variant from Doornik 2005). Most of the arguments are -/// directly from the paper: -/// -/// * `rng`: source of randomness -/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0. -/// * `X`: the $x_i$ abscissae. -/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$) -/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$ -/// * `pdf`: the probability density function -/// * `zero_case`: manual sampling from the tail when we chose the -/// bottom box (i.e. i == 0) - -// the perf improvement (25-50%) is definitely worth the extra code -// size from force-inlining. -#[inline(always)] -fn ziggurat<R:Rng>( - rng: &mut R, - symmetric: bool, - x_tab: ziggurat_tables::ZigTable, - f_tab: ziggurat_tables::ZigTable, - pdf: 'static |f64| -> f64, - zero_case: 'static |&mut R, f64| -> f64) - -> f64 { - static SCALE: f64 = (1u64 << 53) as f64; - loop { - // reimplement the f64 generation as an optimisation suggested - // by the Doornik paper: we have a lot of precision-space - // (i.e. there are 11 bits of the 64 of a u64 to use after - // creating a f64), so we might as well reuse some to save - // generating a whole extra random number. (Seems to be 15% - // faster.) - let bits: u64 = rng.gen(); - let i = (bits & 0xff) as uint; - let f = (bits >> 11) as f64 / SCALE; - - // u is either U(-1, 1) or U(0, 1) depending on if this is a - // symmetric distribution or not. - let u = if symmetric {2.0 * f - 1.0} else {f}; - let x = u * x_tab[i]; - - let test_x = if symmetric {num::abs(x)} else {x}; - - // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i]) - if test_x < x_tab[i + 1] { - return x; - } - if i == 0 { - return zero_case(rng, u); - } - // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1 - if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen() < pdf(x) { - return x; - } - } -} - -#[cfg(test)] -mod tests { - use prelude::*; - use rand::*; - use super::*; - - #[deriving(Eq, Show)] - struct ConstRand(uint); - impl Rand for ConstRand { - fn rand<R: Rng>(_: &mut R) -> ConstRand { - ConstRand(0) - } - } - - // 0, 1, 2, 3, ... - struct CountingRng { i: u32 } - impl Rng for CountingRng { - fn next_u32(&mut self) -> u32 { - self.i += 1; - self.i - 1 - } - fn next_u64(&mut self) -> u64 { - self.next_u32() as u64 - } - } - - #[test] - fn test_rand_sample() { - let mut rand_sample = RandSample::<ConstRand>; - - assert_eq!(rand_sample.sample(&mut task_rng()), ConstRand(0)); - assert_eq!(rand_sample.ind_sample(&mut task_rng()), ConstRand(0)); - } - #[test] - fn test_weighted_choice() { - // this makes assumptions about the internal implementation of - // WeightedChoice, specifically: it doesn't reorder the items, - // it doesn't do weird things to the RNG (so 0 maps to 0, 1 to - // 1, internally; modulo a modulo operation). - - macro_rules! t ( - ($items:expr, $expected:expr) => {{ - let wc = WeightedChoice::new($items); - let expected = $expected; - - let mut rng = CountingRng { i: 0 }; - - for &val in expected.iter() { - assert_eq!(wc.ind_sample(&mut rng), val) - } - }} - ); - - t!(~[Weighted { weight: 1, item: 10}], ~[10]); - - // skip some - t!(~[Weighted { weight: 0, item: 20}, - Weighted { weight: 2, item: 21}, - Weighted { weight: 0, item: 22}, - Weighted { weight: 1, item: 23}], - ~[21,21, 23]); - - // different weights - t!(~[Weighted { weight: 4, item: 30}, - Weighted { weight: 3, item: 31}], - ~[30,30,30,30, 31,31,31]); - - // check that we're binary searching - // correctly with some vectors of odd - // length. - t!(~[Weighted { weight: 1, item: 40}, - Weighted { weight: 1, item: 41}, - Weighted { weight: 1, item: 42}, - Weighted { weight: 1, item: 43}, - Weighted { weight: 1, item: 44}], - ~[40, 41, 42, 43, 44]); - t!(~[Weighted { weight: 1, item: 50}, - Weighted { weight: 1, item: 51}, - Weighted { weight: 1, item: 52}, - Weighted { weight: 1, item: 53}, - Weighted { weight: 1, item: 54}, - Weighted { weight: 1, item: 55}, - Weighted { weight: 1, item: 56}], - ~[50, 51, 52, 53, 54, 55, 56]); - } - - #[test] #[should_fail] - fn test_weighted_choice_no_items() { - WeightedChoice::<int>::new(~[]); - } - #[test] #[should_fail] - fn test_weighted_choice_zero_weight() { - WeightedChoice::new(~[Weighted { weight: 0, item: 0}, - Weighted { weight: 0, item: 1}]); - } - #[test] #[should_fail] - fn test_weighted_choice_weight_overflows() { - let x = (-1) as uint / 2; // x + x + 2 is the overflow - WeightedChoice::new(~[Weighted { weight: x, item: 0 }, - Weighted { weight: 1, item: 1 }, - Weighted { weight: x, item: 2 }, - Weighted { weight: 1, item: 3 }]); - } -} diff --git a/src/libstd/rand/distributions/normal.rs b/src/libstd/rand/distributions/normal.rs deleted file mode 100644 index b2f952e2a4c..00000000000 --- a/src/libstd/rand/distributions/normal.rs +++ /dev/null @@ -1,210 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The normal and derived distributions. - -use num::Float; -use rand::{Rng, Rand, Open01}; -use rand::distributions::{ziggurat, ziggurat_tables, Sample, IndependentSample}; - -/// A wrapper around an `f64` to generate N(0, 1) random numbers -/// (a.k.a. a standard normal, or Gaussian). -/// -/// See `Normal` for the general normal distribution. That this has to -/// be unwrapped before use as an `f64` (using either `*` or -/// `cast::transmute` is safe). -/// -/// Implemented via the ZIGNOR variant[1] of the Ziggurat method. -/// -/// [1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to -/// Generate Normal Random -/// Samples*](http://www.doornik.com/research/ziggurat.pdf). Nuffield -/// College, Oxford -pub struct StandardNormal(f64); - -impl Rand for StandardNormal { - fn rand<R:Rng>(rng: &mut R) -> StandardNormal { - #[inline] - fn pdf(x: f64) -> f64 { - (-x*x/2.0).exp() - } - #[inline] - fn zero_case<R:Rng>(rng: &mut R, u: f64) -> f64 { - // compute a random number in the tail by hand - - // strange initial conditions, because the loop is not - // do-while, so the condition should be true on the first - // run, they get overwritten anyway (0 < 1, so these are - // good). - let mut x = 1.0f64; - let mut y = 0.0f64; - - while -2.0 * y < x * x { - let Open01(x_) = rng.gen::<Open01<f64>>(); - let Open01(y_) = rng.gen::<Open01<f64>>(); - - x = x_.ln() / ziggurat_tables::ZIG_NORM_R; - y = y_.ln(); - } - - if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x } - } - - StandardNormal(ziggurat( - rng, - true, // this is symmetric - &ziggurat_tables::ZIG_NORM_X, - &ziggurat_tables::ZIG_NORM_F, - pdf, zero_case)) - } -} - -/// The normal distribution `N(mean, std_dev**2)`. -/// -/// This uses the ZIGNOR variant of the Ziggurat method, see -/// `StandardNormal` for more details. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{Normal, IndependentSample}; -/// -/// // mean 2, standard deviation 3 -/// let normal = Normal::new(2.0, 3.0); -/// let v = normal.ind_sample(&mut rand::task_rng()); -/// println!("{} is from a N(2, 9) distribution", v) -/// ``` -pub struct Normal { - priv mean: f64, - priv std_dev: f64 -} - -impl Normal { - /// Construct a new `Normal` distribution with the given mean and - /// standard deviation. Fails if `std_dev < 0`. - pub fn new(mean: f64, std_dev: f64) -> Normal { - assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0"); - Normal { - mean: mean, - std_dev: std_dev - } - } -} -impl Sample<f64> for Normal { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for Normal { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - let StandardNormal(n) = rng.gen::<StandardNormal>(); - self.mean + self.std_dev * n - } -} - - -/// The log-normal distribution `ln N(mean, std_dev**2)`. -/// -/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, -/// std_dev**2)` distributed. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{LogNormal, IndependentSample}; -/// -/// // mean 2, standard deviation 3 -/// let log_normal = LogNormal::new(2.0, 3.0); -/// let v = log_normal.ind_sample(&mut rand::task_rng()); -/// println!("{} is from an ln N(2, 9) distribution", v) -/// ``` -pub struct LogNormal { - priv norm: Normal -} - -impl LogNormal { - /// Construct a new `LogNormal` distribution with the given mean - /// and standard deviation. Fails if `std_dev < 0`. - pub fn new(mean: f64, std_dev: f64) -> LogNormal { - assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0"); - LogNormal { norm: Normal::new(mean, std_dev) } - } -} -impl Sample<f64> for LogNormal { - fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) } -} -impl IndependentSample<f64> for LogNormal { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 { - self.norm.ind_sample(rng).exp() - } -} - -#[cfg(test)] -mod tests { - use prelude::*; - use rand::*; - use super::*; - use rand::distributions::*; - - #[test] - fn test_normal() { - let mut norm = Normal::new(10.0, 10.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - norm.sample(&mut rng); - norm.ind_sample(&mut rng); - } - } - #[test] - #[should_fail] - fn test_normal_invalid_sd() { - Normal::new(10.0, -1.0); - } - - - #[test] - fn test_log_normal() { - let mut lnorm = LogNormal::new(10.0, 10.0); - let mut rng = task_rng(); - for _ in range(0, 1000) { - lnorm.sample(&mut rng); - lnorm.ind_sample(&mut rng); - } - } - #[test] - #[should_fail] - fn test_log_normal_invalid_sd() { - LogNormal::new(10.0, -1.0); - } -} - -#[cfg(test)] -mod bench { - extern crate test; - use self::test::BenchHarness; - use mem::size_of; - use prelude::*; - use rand::{XorShiftRng, RAND_BENCH_N}; - use rand::distributions::*; - use super::*; - - #[bench] - fn rand_normal(bh: &mut BenchHarness) { - let mut rng = XorShiftRng::new(); - let mut normal = Normal::new(-2.71828, 3.14159); - - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - normal.sample(&mut rng); - } - }); - bh.bytes = size_of::<f64>() as u64 * RAND_BENCH_N; - } -} diff --git a/src/libstd/rand/distributions/range.rs b/src/libstd/rand/distributions/range.rs deleted file mode 100644 index 8141b3d3e89..00000000000 --- a/src/libstd/rand/distributions/range.rs +++ /dev/null @@ -1,234 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! Generating numbers between two others. - -// this is surprisingly complicated to be both generic & correct - -use cmp::Ord; -use num::Bounded; -use rand::Rng; -use rand::distributions::{Sample, IndependentSample}; - -/// Sample values uniformly between two bounds. -/// -/// This gives a uniform distribution (assuming the RNG used to sample -/// it is itself uniform & the `SampleRange` implementation for the -/// given type is correct), even for edge cases like `low = 0u8`, -/// `high = 170u8`, for which a naive modulo operation would return -/// numbers less than 85 with double the probability to those greater -/// than 85. -/// -/// Types should attempt to sample in `[low, high)`, i.e., not -/// including `high`, but this may be very difficult. All the -/// primitive integer types satisfy this property, and the float types -/// normally satisfy it, but rounding may mean `high` can occur. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::distributions::{IndependentSample, Range}; -/// -/// fn main() { -/// let between = Range::new(10u, 10000u); -/// let mut rng = rand::task_rng(); -/// let mut sum = 0; -/// for _ in range(0, 1000) { -/// sum += between.ind_sample(&mut rng); -/// } -/// println!("{}", sum); -/// } -/// ``` -pub struct Range<X> { - priv low: X, - priv range: X, - priv accept_zone: X -} - -impl<X: SampleRange + Ord> Range<X> { - /// Create a new `Range` instance that samples uniformly from - /// `[low, high)`. Fails if `low >= high`. - pub fn new(low: X, high: X) -> Range<X> { - assert!(low < high, "Range::new called with `low >= high`"); - SampleRange::construct_range(low, high) - } -} - -impl<Sup: SampleRange> Sample<Sup> for Range<Sup> { - #[inline] - fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) } -} -impl<Sup: SampleRange> IndependentSample<Sup> for Range<Sup> { - fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup { - SampleRange::sample_range(self, rng) - } -} - -/// The helper trait for types that have a sensible way to sample -/// uniformly between two values. This should not be used directly, -/// and is only to facilitate `Range`. -pub trait SampleRange { - /// Construct the `Range` object that `sample_range` - /// requires. This should not ever be called directly, only via - /// `Range::new`, which will check that `low < high`, so this - /// function doesn't have to repeat the check. - fn construct_range(low: Self, high: Self) -> Range<Self>; - - /// Sample a value from the given `Range` with the given `Rng` as - /// a source of randomness. - fn sample_range<R: Rng>(r: &Range<Self>, rng: &mut R) -> Self; -} - -macro_rules! integer_impl { - ($ty:ty, $unsigned:ty) => { - impl SampleRange for $ty { - // we play free and fast with unsigned vs signed here - // (when $ty is signed), but that's fine, since the - // contract of this macro is for $ty and $unsigned to be - // "bit-equal", so casting between them is a no-op & a - // bijection. - - fn construct_range(low: $ty, high: $ty) -> Range<$ty> { - let range = high as $unsigned - low as $unsigned; - let unsigned_max: $unsigned = Bounded::max_value(); - - // this is the largest number that fits into $unsigned - // that `range` divides evenly, so, if we've sampled - // `n` uniformly from this region, then `n % range` is - // uniform in [0, range) - let zone = unsigned_max - unsigned_max % range; - - Range { - low: low, - range: range as $ty, - accept_zone: zone as $ty - } - } - #[inline] - fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { - loop { - // rejection sample - let v = rng.gen::<$unsigned>(); - // until we find something that fits into the - // region which r.range evenly divides (this will - // be uniformly distributed) - if v < r.accept_zone as $unsigned { - // and return it, with some adjustments - return r.low + (v % r.range as $unsigned) as $ty; - } - } - } - } - } -} - -integer_impl! { i8, u8 } -integer_impl! { i16, u16 } -integer_impl! { i32, u32 } -integer_impl! { i64, u64 } -integer_impl! { int, uint } -integer_impl! { u8, u8 } -integer_impl! { u16, u16 } -integer_impl! { u32, u32 } -integer_impl! { u64, u64 } -integer_impl! { uint, uint } - -macro_rules! float_impl { - ($ty:ty) => { - impl SampleRange for $ty { - fn construct_range(low: $ty, high: $ty) -> Range<$ty> { - Range { - low: low, - range: high - low, - accept_zone: 0.0 // unused - } - } - fn sample_range<R: Rng>(r: &Range<$ty>, rng: &mut R) -> $ty { - r.low + r.range * rng.gen() - } - } - } -} - -float_impl! { f32 } -float_impl! { f64 } - -#[cfg(test)] -mod tests { - use prelude::*; - use super::*; - use rand::*; - use rand::distributions::*; - use num::Bounded; - - #[should_fail] - #[test] - fn test_range_bad_limits_equal() { - Range::new(10, 10); - } - #[should_fail] - #[test] - fn test_range_bad_limits_flipped() { - Range::new(10, 5); - } - - #[test] - fn test_integers() { - let mut rng = task_rng(); - macro_rules! t ( - ($($ty:ty),*) => {{ - $( - let v: &[($ty, $ty)] = [(0, 10), - (10, 127), - (Bounded::min_value(), Bounded::max_value())]; - for &(low, high) in v.iter() { - let mut sampler: Range<$ty> = Range::new(low, high); - for _ in range(0, 1000) { - let v = sampler.sample(&mut rng); - assert!(low <= v && v < high); - let v = sampler.ind_sample(&mut rng); - assert!(low <= v && v < high); - } - } - )* - }} - ); - t!(i8, i16, i32, i64, int, - u8, u16, u32, u64, uint) - } - - #[test] - fn test_floats() { - let mut rng = task_rng(); - macro_rules! t ( - ($($ty:ty),*) => {{ - $( - let v: &[($ty, $ty)] = [(0.0, 100.0), - (-1e35, -1e25), - (1e-35, 1e-25), - (-1e35, 1e35)]; - for &(low, high) in v.iter() { - let mut sampler: Range<$ty> = Range::new(low, high); - for _ in range(0, 1000) { - let v = sampler.sample(&mut rng); - assert!(low <= v && v < high); - let v = sampler.ind_sample(&mut rng); - assert!(low <= v && v < high); - } - } - )* - }} - ); - - t!(f32, f64) - } - -} diff --git a/src/libstd/rand/distributions/ziggurat_tables.rs b/src/libstd/rand/distributions/ziggurat_tables.rs deleted file mode 100644 index 049ef3dbb59..00000000000 --- a/src/libstd/rand/distributions/ziggurat_tables.rs +++ /dev/null @@ -1,280 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -// Tables for distributions which are sampled using the ziggurat -// algorithm. Autogenerated by `ziggurat_tables.py`. - -pub type ZigTable = &'static [f64, .. 257]; -pub static ZIG_NORM_R: f64 = 3.654152885361008796; -pub static ZIG_NORM_X: [f64, .. 257] = - [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074, - 3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434, - 2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548, - 2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056, - 2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570, - 2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761, - 2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318, - 2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520, - 2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952, - 2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565, - 2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760, - 2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995, - 2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268, - 2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957, - 2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778, - 2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715, - 2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244, - 1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896, - 1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257, - 1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081, - 1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281, - 1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566, - 1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199, - 1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933, - 1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012, - 1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086, - 1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338, - 1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526, - 1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427, - 1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339, - 1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456, - 1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553, - 1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404, - 1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369, - 1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830, - 1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425, - 1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534, - 1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964, - 1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606, - 1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679, - 1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728, - 1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732, - 1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903, - 1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552, - 1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650, - 1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240, - 1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975, - 1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151, - 1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714, - 1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538, - 1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441, - 1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750, - 0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130, - 0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997, - 0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550, - 0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752, - 0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785, - 0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653, - 0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448, - 0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928, - 0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262, - 0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393, - 0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746, - 0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806, - 0.000000000000000000]; -pub static ZIG_NORM_F: [f64, .. 257] = - [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872, - 0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100, - 0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839, - 0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237, - 0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690, - 0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918, - 0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664, - 0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916, - 0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854, - 0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965, - 0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509, - 0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229, - 0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627, - 0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880, - 0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014, - 0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349, - 0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352, - 0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926, - 0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563, - 0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071, - 0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654, - 0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926, - 0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112, - 0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651, - 0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589, - 0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525, - 0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988, - 0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150, - 0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837, - 0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316, - 0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984, - 0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274, - 0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396, - 0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099, - 0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340, - 0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515, - 0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344, - 0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958, - 0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668, - 0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784, - 0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519, - 0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750, - 0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481, - 0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788, - 0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658, - 0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142, - 0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700, - 0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941, - 0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916, - 0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473, - 0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719, - 0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205, - 0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991, - 0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357, - 0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376, - 0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409, - 0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437, - 0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500, - 0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902, - 0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935, - 0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077, - 0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839, - 0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247, - 0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328, - 1.000000000000000000]; -pub static ZIG_EXP_R: f64 = 7.697117470131050077; -pub static ZIG_EXP_X: [f64, .. 257] = - [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696, - 6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488, - 5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530, - 4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380, - 4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857, - 4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762, - 3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744, - 3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770, - 3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608, - 3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405, - 3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160, - 3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481, - 3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601, - 2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825, - 2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780, - 2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752, - 2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489, - 2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970, - 2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815, - 2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886, - 2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372, - 2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213, - 2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027, - 2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289, - 2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526, - 2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563, - 1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943, - 1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242, - 1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954, - 1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014, - 1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566, - 1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896, - 1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334, - 1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892, - 1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092, - 1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058, - 1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504, - 1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137, - 1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189, - 1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117, - 1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330, - 1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124, - 1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677, - 1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511, - 1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813, - 1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209, - 1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735, - 0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509, - 0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311, - 0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066, - 0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206, - 0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430, - 0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102, - 0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959, - 0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947, - 0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030, - 0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626, - 0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398, - 0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235, - 0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765, - 0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122, - 0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703, - 0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842, - 0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570, - 0.000000000000000000]; -pub static ZIG_EXP_F: [f64, .. 257] = - [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573, - 0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797, - 0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991, - 0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981, - 0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943, - 0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355, - 0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581, - 0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221, - 0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622, - 0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431, - 0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139, - 0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289, - 0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379, - 0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030, - 0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660, - 0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816, - 0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752, - 0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435, - 0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146, - 0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197, - 0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213, - 0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145, - 0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283, - 0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641, - 0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671, - 0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602, - 0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146, - 0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839, - 0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129, - 0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081, - 0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829, - 0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083, - 0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189, - 0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654, - 0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628, - 0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956, - 0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560, - 0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543, - 0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173, - 0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967, - 0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746, - 0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252, - 0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185, - 0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223, - 0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717, - 0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449, - 0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379, - 0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056, - 0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350, - 0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209, - 0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907, - 0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836, - 0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708, - 0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881, - 0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931, - 0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056, - 0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150, - 0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560, - 0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398, - 0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177, - 0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456, - 0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838, - 0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101, - 0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477, - 1.000000000000000000]; diff --git a/src/libstd/rand/isaac.rs b/src/libstd/rand/isaac.rs deleted file mode 100644 index 9871207a91e..00000000000 --- a/src/libstd/rand/isaac.rs +++ /dev/null @@ -1,535 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The ISAAC random number generator. - -use rand::{Rng, SeedableRng, OSRng}; -use iter::{Iterator, range, range_step, Repeat}; -use option::{None, Some}; -use vec::{raw, MutableVector, ImmutableVector}; -use mem; - -static RAND_SIZE_LEN: u32 = 8; -static RAND_SIZE: u32 = 1 << RAND_SIZE_LEN; - -/// A random number generator that uses the ISAAC algorithm[1]. -/// -/// The ISAAC algorithm is generally accepted as suitable for -/// cryptographic purposes, but this implementation has not be -/// verified as such. Prefer a generator like `OSRng` that defers to -/// the operating system for cases that need high security. -/// -/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number -/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html) -pub struct IsaacRng { - priv cnt: u32, - priv rsl: [u32, .. RAND_SIZE], - priv mem: [u32, .. RAND_SIZE], - priv a: u32, - priv b: u32, - priv c: u32 -} -static EMPTY: IsaacRng = IsaacRng { - cnt: 0, - rsl: [0, .. RAND_SIZE], - mem: [0, .. RAND_SIZE], - a: 0, b: 0, c: 0 -}; - -impl IsaacRng { - /// Create an ISAAC random number generator with a random seed. - pub fn new() -> IsaacRng { - let mut rng = EMPTY; - - unsafe { - let ptr = rng.rsl.as_mut_ptr(); - - raw::mut_buf_as_slice(ptr as *mut u8, mem::size_of_val(&rng.rsl), |slice| { - OSRng::new().fill_bytes(slice); - }) - } - - rng.init(true); - rng - } - - /// Create an ISAAC random number generator using the default - /// fixed seed. - pub fn new_unseeded() -> IsaacRng { - let mut rng = EMPTY; - rng.init(false); - rng - } - - /// Initialises `self`. If `use_rsl` is true, then use the current value - /// of `rsl` as a seed, otherwise construct one algorithmically (not - /// randomly). - fn init(&mut self, use_rsl: bool) { - let mut a = 0x9e3779b9; - let mut b = a; - let mut c = a; - let mut d = a; - let mut e = a; - let mut f = a; - let mut g = a; - let mut h = a; - - macro_rules! mix( - () => {{ - a^=b<<11; d+=a; b+=c; - b^=c>>2; e+=b; c+=d; - c^=d<<8; f+=c; d+=e; - d^=e>>16; g+=d; e+=f; - e^=f<<10; h+=e; f+=g; - f^=g>>4; a+=f; g+=h; - g^=h<<8; b+=g; h+=a; - h^=a>>9; c+=h; a+=b; - }} - ); - - for _ in range(0, 4) { mix!(); } - - if use_rsl { - macro_rules! memloop ( - ($arr:expr) => {{ - for i in range_step(0u32, RAND_SIZE, 8) { - a+=$arr[i ]; b+=$arr[i+1]; - c+=$arr[i+2]; d+=$arr[i+3]; - e+=$arr[i+4]; f+=$arr[i+5]; - g+=$arr[i+6]; h+=$arr[i+7]; - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - }} - ); - - memloop!(self.rsl); - memloop!(self.mem); - } else { - for i in range_step(0u32, RAND_SIZE, 8) { - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - } - - self.isaac(); - } - - /// Refills the output buffer (`self.rsl`) - #[inline] - fn isaac(&mut self) { - self.c += 1; - // abbreviations - let mut a = self.a; - let mut b = self.b + self.c; - - static MIDPOINT: uint = RAND_SIZE as uint / 2; - - macro_rules! ind (($x:expr) => { - self.mem[($x >> 2) & (RAND_SIZE - 1)] - }); - macro_rules! rngstep( - ($j:expr, $shift:expr) => {{ - let base = $j; - let mix = if $shift < 0 { - a >> -$shift as uint - } else { - a << $shift as uint - }; - - let x = self.mem[base + mr_offset]; - a = (a ^ mix) + self.mem[base + m2_offset]; - let y = ind!(x) + a + b; - self.mem[base + mr_offset] = y; - - b = ind!(y >> RAND_SIZE_LEN) + x; - self.rsl[base + mr_offset] = b; - }} - ); - - let r = [(0, MIDPOINT), (MIDPOINT, 0)]; - for &(mr_offset, m2_offset) in r.iter() { - for i in range_step(0u, MIDPOINT, 4) { - rngstep!(i + 0, 13); - rngstep!(i + 1, -6); - rngstep!(i + 2, 2); - rngstep!(i + 3, -16); - } - } - - self.a = a; - self.b = b; - self.cnt = RAND_SIZE; - } -} - -impl Rng for IsaacRng { - #[inline] - fn next_u32(&mut self) -> u32 { - if self.cnt == 0 { - // make some more numbers - self.isaac(); - } - self.cnt -= 1; - self.rsl[self.cnt] - } -} - -impl<'a> SeedableRng<&'a [u32]> for IsaacRng { - fn reseed(&mut self, seed: &'a [u32]) { - // make the seed into [seed[0], seed[1], ..., seed[seed.len() - // - 1], 0, 0, ...], to fill rng.rsl. - let seed_iter = seed.iter().map(|&x| x).chain(Repeat::new(0u32)); - - for (rsl_elem, seed_elem) in self.rsl.mut_iter().zip(seed_iter) { - *rsl_elem = seed_elem; - } - self.cnt = 0; - self.a = 0; - self.b = 0; - self.c = 0; - - self.init(true); - } - - /// Create an ISAAC random number generator with a seed. This can - /// be any length, although the maximum number of elements used is - /// 256 and any more will be silently ignored. A generator - /// constructed with a given seed will generate the same sequence - /// of values as all other generators constructed with that seed. - fn from_seed(seed: &'a [u32]) -> IsaacRng { - let mut rng = EMPTY; - rng.reseed(seed); - rng - } -} - - -static RAND_SIZE_64_LEN: uint = 8; -static RAND_SIZE_64: uint = 1 << RAND_SIZE_64_LEN; - -/// A random number generator that uses ISAAC-64[1], the 64-bit -/// variant of the ISAAC algorithm. -/// -/// The ISAAC algorithm is generally accepted as suitable for -/// cryptographic purposes, but this implementation has not be -/// verified as such. Prefer a generator like `OSRng` that defers to -/// the operating system for cases that need high security. -/// -/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number -/// generator*](http://www.burtleburtle.net/bob/rand/isaacafa.html) -pub struct Isaac64Rng { - priv cnt: uint, - priv rsl: [u64, .. RAND_SIZE_64], - priv mem: [u64, .. RAND_SIZE_64], - priv a: u64, - priv b: u64, - priv c: u64, -} - -static EMPTY_64: Isaac64Rng = Isaac64Rng { - cnt: 0, - rsl: [0, .. RAND_SIZE_64], - mem: [0, .. RAND_SIZE_64], - a: 0, b: 0, c: 0, -}; - -impl Isaac64Rng { - /// Create a 64-bit ISAAC random number generator with a random - /// seed. - pub fn new() -> Isaac64Rng { - let mut rng = EMPTY_64; - - unsafe { - let ptr = rng.rsl.as_mut_ptr(); - - raw::mut_buf_as_slice(ptr as *mut u8, mem::size_of_val(&rng.rsl), |slice| { - OSRng::new().fill_bytes(slice); - }) - } - - rng.init(true); - rng - } - - /// Create a 64-bit ISAAC random number generator using the - /// default fixed seed. - pub fn new_unseeded() -> Isaac64Rng { - let mut rng = EMPTY_64; - rng.init(false); - rng - } - - /// Initialises `self`. If `use_rsl` is true, then use the current value - /// of `rsl` as a seed, otherwise construct one algorithmically (not - /// randomly). - fn init(&mut self, use_rsl: bool) { - macro_rules! init ( - ($var:ident) => ( - let mut $var = 0x9e3779b97f4a7c13; - ) - ); - init!(a); init!(b); init!(c); init!(d); - init!(e); init!(f); init!(g); init!(h); - - macro_rules! mix( - () => {{ - a-=e; f^=h>>9; h+=a; - b-=f; g^=a<<9; a+=b; - c-=g; h^=b>>23; b+=c; - d-=h; a^=c<<15; c+=d; - e-=a; b^=d>>14; d+=e; - f-=b; c^=e<<20; e+=f; - g-=c; d^=f>>17; f+=g; - h-=d; e^=g<<14; g+=h; - }} - ); - - for _ in range(0, 4) { mix!(); } - if use_rsl { - macro_rules! memloop ( - ($arr:expr) => {{ - for i in range(0, RAND_SIZE_64 / 8).map(|i| i * 8) { - a+=$arr[i ]; b+=$arr[i+1]; - c+=$arr[i+2]; d+=$arr[i+3]; - e+=$arr[i+4]; f+=$arr[i+5]; - g+=$arr[i+6]; h+=$arr[i+7]; - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - }} - ); - - memloop!(self.rsl); - memloop!(self.mem); - } else { - for i in range(0, RAND_SIZE_64 / 8).map(|i| i * 8) { - mix!(); - self.mem[i ]=a; self.mem[i+1]=b; - self.mem[i+2]=c; self.mem[i+3]=d; - self.mem[i+4]=e; self.mem[i+5]=f; - self.mem[i+6]=g; self.mem[i+7]=h; - } - } - - self.isaac64(); - } - - /// Refills the output buffer (`self.rsl`) - fn isaac64(&mut self) { - self.c += 1; - // abbreviations - let mut a = self.a; - let mut b = self.b + self.c; - static MIDPOINT: uint = RAND_SIZE_64 / 2; - static MP_VEC: [(uint, uint), .. 2] = [(0,MIDPOINT), (MIDPOINT, 0)]; - macro_rules! ind ( - ($x:expr) => { - *self.mem.unsafe_ref(($x as uint >> 3) & (RAND_SIZE_64 - 1)) - } - ); - macro_rules! rngstep( - ($j:expr, $shift:expr) => {{ - let base = base + $j; - let mix = a ^ (if $shift < 0 { - a >> -$shift as uint - } else { - a << $shift as uint - }); - let mix = if $j == 0 {!mix} else {mix}; - - unsafe { - let x = *self.mem.unsafe_ref(base + mr_offset); - a = mix + *self.mem.unsafe_ref(base + m2_offset); - let y = ind!(x) + a + b; - self.mem.unsafe_set(base + mr_offset, y); - - b = ind!(y >> RAND_SIZE_64_LEN) + x; - self.rsl.unsafe_set(base + mr_offset, b); - } - }} - ); - - for &(mr_offset, m2_offset) in MP_VEC.iter() { - for base in range(0, MIDPOINT / 4).map(|i| i * 4) { - rngstep!(0, 21); - rngstep!(1, -5); - rngstep!(2, 12); - rngstep!(3, -33); - } - } - - self.a = a; - self.b = b; - self.cnt = RAND_SIZE_64; - } -} - -impl Rng for Isaac64Rng { - // FIXME #7771: having next_u32 like this should be unnecessary - #[inline] - fn next_u32(&mut self) -> u32 { - self.next_u64() as u32 - } - - #[inline] - fn next_u64(&mut self) -> u64 { - if self.cnt == 0 { - // make some more numbers - self.isaac64(); - } - self.cnt -= 1; - unsafe { *self.rsl.unsafe_ref(self.cnt) } - } -} - -impl<'a> SeedableRng<&'a [u64]> for Isaac64Rng { - fn reseed(&mut self, seed: &'a [u64]) { - // make the seed into [seed[0], seed[1], ..., seed[seed.len() - // - 1], 0, 0, ...], to fill rng.rsl. - let seed_iter = seed.iter().map(|&x| x).chain(Repeat::new(0u64)); - - for (rsl_elem, seed_elem) in self.rsl.mut_iter().zip(seed_iter) { - *rsl_elem = seed_elem; - } - self.cnt = 0; - self.a = 0; - self.b = 0; - self.c = 0; - - self.init(true); - } - - /// Create an ISAAC random number generator with a seed. This can - /// be any length, although the maximum number of elements used is - /// 256 and any more will be silently ignored. A generator - /// constructed with a given seed will generate the same sequence - /// of values as all other generators constructed with that seed. - fn from_seed(seed: &'a [u64]) -> Isaac64Rng { - let mut rng = EMPTY_64; - rng.reseed(seed); - rng - } -} - -#[cfg(test)] -mod test { - use super::*; - use rand::{Rng, SeedableRng, OSRng}; - use prelude::*; - use vec; - - #[test] - fn test_rng_32_rand_seeded() { - let s = OSRng::new().gen_vec::<u32>(256); - let mut ra: IsaacRng = SeedableRng::from_seed(s.as_slice()); - let mut rb: IsaacRng = SeedableRng::from_seed(s.as_slice()); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - #[test] - fn test_rng_64_rand_seeded() { - let s = OSRng::new().gen_vec::<u64>(256); - let mut ra: Isaac64Rng = SeedableRng::from_seed(s.as_slice()); - let mut rb: Isaac64Rng = SeedableRng::from_seed(s.as_slice()); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - - #[test] - fn test_rng_32_seeded() { - let seed = &[1, 23, 456, 7890, 12345]; - let mut ra: IsaacRng = SeedableRng::from_seed(seed); - let mut rb: IsaacRng = SeedableRng::from_seed(seed); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - #[test] - fn test_rng_64_seeded() { - let seed = &[1, 23, 456, 7890, 12345]; - let mut ra: Isaac64Rng = SeedableRng::from_seed(seed); - let mut rb: Isaac64Rng = SeedableRng::from_seed(seed); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - - #[test] - fn test_rng_32_reseed() { - let s = OSRng::new().gen_vec::<u32>(256); - let mut r: IsaacRng = SeedableRng::from_seed(s.as_slice()); - let string1 = r.gen_ascii_str(100); - - r.reseed(s); - - let string2 = r.gen_ascii_str(100); - assert_eq!(string1, string2); - } - #[test] - fn test_rng_64_reseed() { - let s = OSRng::new().gen_vec::<u64>(256); - let mut r: Isaac64Rng = SeedableRng::from_seed(s.as_slice()); - let string1 = r.gen_ascii_str(100); - - r.reseed(s); - - let string2 = r.gen_ascii_str(100); - assert_eq!(string1, string2); - } - - #[test] - fn test_rng_32_true_values() { - let seed = &[1, 23, 456, 7890, 12345]; - let mut ra: IsaacRng = SeedableRng::from_seed(seed); - // Regression test that isaac is actually using the above vector - let v = vec::from_fn(10, |_| ra.next_u32()); - assert_eq!(v, - ~[2558573138, 873787463, 263499565, 2103644246, 3595684709, - 4203127393, 264982119, 2765226902, 2737944514, 3900253796]); - - let seed = &[12345, 67890, 54321, 9876]; - let mut rb: IsaacRng = SeedableRng::from_seed(seed); - // skip forward to the 10000th number - for _ in range(0, 10000) { rb.next_u32(); } - - let v = vec::from_fn(10, |_| rb.next_u32()); - assert_eq!(v, - ~[3676831399, 3183332890, 2834741178, 3854698763, 2717568474, - 1576568959, 3507990155, 179069555, 141456972, 2478885421]); - } - #[test] - fn test_rng_64_true_values() { - let seed = &[1, 23, 456, 7890, 12345]; - let mut ra: Isaac64Rng = SeedableRng::from_seed(seed); - // Regression test that isaac is actually using the above vector - let v = vec::from_fn(10, |_| ra.next_u64()); - assert_eq!(v, - ~[547121783600835980, 14377643087320773276, 17351601304698403469, - 1238879483818134882, 11952566807690396487, 13970131091560099343, - 4469761996653280935, 15552757044682284409, 6860251611068737823, - 13722198873481261842]); - - let seed = &[12345, 67890, 54321, 9876]; - let mut rb: Isaac64Rng = SeedableRng::from_seed(seed); - // skip forward to the 10000th number - for _ in range(0, 10000) { rb.next_u64(); } - - let v = vec::from_fn(10, |_| rb.next_u64()); - assert_eq!(v, - ~[18143823860592706164, 8491801882678285927, 2699425367717515619, - 17196852593171130876, 2606123525235546165, 15790932315217671084, - 596345674630742204, 9947027391921273664, 11788097613744130851, - 10391409374914919106]); - } -} diff --git a/src/libstd/rand/mod.rs b/src/libstd/rand/mod.rs deleted file mode 100644 index 20bce8d2058..00000000000 --- a/src/libstd/rand/mod.rs +++ /dev/null @@ -1,906 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -/*! -Utilities for random number generation - -The key functions are `random()` and `Rng::gen()`. These are polymorphic -and so can be used to generate any type that implements `Rand`. Type inference -means that often a simple call to `rand::random()` or `rng.gen()` will -suffice, but sometimes an annotation is required, e.g. `rand::random::<f64>()`. - -See the `distributions` submodule for sampling random numbers from -distributions like normal and exponential. - -# Task-local RNG - -There is built-in support for a RNG associated with each task stored -in task-local storage. This RNG can be accessed via `task_rng`, or -used implicitly via `random`. This RNG is normally randomly seeded -from an operating-system source of randomness, e.g. `/dev/urandom` on -Unix systems, and will automatically reseed itself from this source -after generating 32 KiB of random data. - -# Cryptographic security - -An application that requires random numbers for cryptographic purposes -should prefer `OSRng`, which reads randomness from one of the source -that the operating system provides (e.g. `/dev/urandom` on -Unixes). The other random number generators provided by this module -are either known to be insecure (`XorShiftRng`), or are not verified -to be secure (`IsaacRng`, `Isaac64Rng` and `StdRng`). - -*Note*: on Linux, `/dev/random` is more secure than `/dev/urandom`, -but it is a blocking RNG, and will wait until it has determined that -it has collected enough entropy to fulfill a request for random -data. It can be used with the `Rng` trait provided by this module by -opening the file and passing it to `reader::ReaderRng`. Since it -blocks, `/dev/random` should only be used to retrieve small amounts of -randomness. - -# Examples - -```rust -use std::rand; -use std::rand::Rng; - -let mut rng = rand::rng(); -if rng.gen() { // bool - println!("int: {}, uint: {}", rng.gen::<int>(), rng.gen::<uint>()) -} - ``` - -```rust -use std::rand; - -let tuple_ptr = rand::random::<~(f64, char)>(); -println!("{:?}", tuple_ptr) - ``` -*/ - -use cast; -use cmp::Ord; -use container::Container; -use iter::{Iterator, range}; -use kinds::marker; -use local_data; -use prelude::*; -use str; -use vec; - -pub use self::isaac::{IsaacRng, Isaac64Rng}; -pub use self::os::OSRng; - -use self::distributions::{Range, IndependentSample}; -use self::distributions::range::SampleRange; - -pub mod distributions; -pub mod isaac; -pub mod os; -pub mod reader; -pub mod reseeding; -mod rand_impls; - -/// A type that can be randomly generated using an `Rng`. -pub trait Rand { - /// Generates a random instance of this type using the specified source of - /// randomness. - fn rand<R: Rng>(rng: &mut R) -> Self; -} - -/// A random number generator. -pub trait Rng { - /// Return the next random u32. - /// - /// This rarely needs to be called directly, prefer `r.gen()` to - /// `r.next_u32()`. - // FIXME #7771: Should be implemented in terms of next_u64 - fn next_u32(&mut self) -> u32; - - /// Return the next random u64. - /// - /// By default this is implemented in terms of `next_u32`. An - /// implementation of this trait must provide at least one of - /// these two methods. Similarly to `next_u32`, this rarely needs - /// to be called directly, prefer `r.gen()` to `r.next_u64()`. - fn next_u64(&mut self) -> u64 { - (self.next_u32() as u64 << 32) | (self.next_u32() as u64) - } - - /// Fill `dest` with random data. - /// - /// This has a default implementation in terms of `next_u64` and - /// `next_u32`, but should be overridden by implementations that - /// offer a more efficient solution than just calling those - /// methods repeatedly. - /// - /// This method does *not* have a requirement to bear any fixed - /// relationship to the other methods, for example, it does *not* - /// have to result in the same output as progressively filling - /// `dest` with `self.gen::<u8>()`, and any such behaviour should - /// not be relied upon. - /// - /// This method should guarantee that `dest` is entirely filled - /// with new data, and may fail if this is impossible - /// (e.g. reading past the end of a file that is being used as the - /// source of randomness). - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut v = [0u8, .. 13579]; - /// task_rng().fill_bytes(v); - /// println!("{:?}", v); - /// ``` - fn fill_bytes(&mut self, dest: &mut [u8]) { - // this could, in theory, be done by transmuting dest to a - // [u64], but this is (1) likely to be undefined behaviour for - // LLVM, (2) has to be very careful about alignment concerns, - // (3) adds more `unsafe` that needs to be checked, (4) - // probably doesn't give much performance gain if - // optimisations are on. - let mut count = 0; - let mut num = 0; - for byte in dest.mut_iter() { - if count == 0 { - // we could micro-optimise here by generating a u32 if - // we only need a few more bytes to fill the vector - // (i.e. at most 4). - num = self.next_u64(); - count = 8; - } - - *byte = (num & 0xff) as u8; - num >>= 8; - count -= 1; - } - } - - /// Return a random value of a `Rand` type. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// let x: uint = rng.gen(); - /// println!("{}", x); - /// println!("{:?}", rng.gen::<(f64, bool)>()); - /// ``` - #[inline(always)] - fn gen<T: Rand>(&mut self) -> T { - Rand::rand(self) - } - - /// Return a random vector of the specified length. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// let x: ~[uint] = rng.gen_vec(10); - /// println!("{:?}", x); - /// println!("{:?}", rng.gen_vec::<(f64, bool)>(5)); - /// ``` - fn gen_vec<T: Rand>(&mut self, len: uint) -> ~[T] { - vec::from_fn(len, |_| self.gen()) - } - - /// Generate a random value in the range [`low`, `high`). Fails if - /// `low >= high`. - /// - /// This is a convenience wrapper around - /// `distributions::Range`. If this function will be called - /// repeatedly with the same arguments, one should use `Range`, as - /// that will amortize the computations that allow for perfect - /// uniformity, as they only happen on initialization. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// let n: uint = rng.gen_range(0u, 10); - /// println!("{}", n); - /// let m: f64 = rng.gen_range(-40.0, 1.3e5); - /// println!("{}", m); - /// ``` - fn gen_range<T: Ord + SampleRange>(&mut self, low: T, high: T) -> T { - assert!(low < high, "Rng.gen_range called with low >= high"); - Range::new(low, high).ind_sample(self) - } - - /// Return a bool with a 1 in n chance of true - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// println!("{:b}", rng.gen_weighted_bool(3)); - /// ``` - fn gen_weighted_bool(&mut self, n: uint) -> bool { - n == 0 || self.gen_range(0, n) == 0 - } - - /// Return a random string of the specified length composed of - /// A-Z,a-z,0-9. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// println!("{}", task_rng().gen_ascii_str(10)); - /// ``` - fn gen_ascii_str(&mut self, len: uint) -> ~str { - static GEN_ASCII_STR_CHARSET: &'static [u8] = bytes!("ABCDEFGHIJKLMNOPQRSTUVWXYZ\ - abcdefghijklmnopqrstuvwxyz\ - 0123456789"); - let mut s = str::with_capacity(len); - for _ in range(0, len) { - s.push_char(self.choose(GEN_ASCII_STR_CHARSET) as char) - } - s - } - - /// Choose an item randomly, failing if `values` is empty. - fn choose<T: Clone>(&mut self, values: &[T]) -> T { - self.choose_option(values).expect("Rng.choose: `values` is empty").clone() - } - - /// Choose `Some(&item)` randomly, returning `None` if values is - /// empty. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let choices = [1, 2, 4, 8, 16, 32]; - /// let mut rng = task_rng(); - /// println!("{:?}", rng.choose_option(choices)); - /// println!("{:?}", rng.choose_option(choices.slice_to(0))); - /// ``` - fn choose_option<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> { - if values.is_empty() { - None - } else { - Some(&values[self.gen_range(0u, values.len())]) - } - } - - /// Shuffle a vec - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// println!("{:?}", task_rng().shuffle(~[1,2,3])); - /// ``` - fn shuffle<T>(&mut self, values: ~[T]) -> ~[T] { - let mut v = values; - self.shuffle_mut(v); - v - } - - /// Shuffle a mutable vector in place. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// let mut y = [1,2,3]; - /// rng.shuffle_mut(y); - /// println!("{:?}", y); - /// rng.shuffle_mut(y); - /// println!("{:?}", y); - /// ``` - fn shuffle_mut<T>(&mut self, values: &mut [T]) { - let mut i = values.len(); - while i >= 2u { - // invariant: elements with index >= i have been locked in place. - i -= 1u; - // lock element i in place. - values.swap(i, self.gen_range(0u, i + 1u)); - } - } - - /// Randomly sample up to `n` elements from an iterator. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{task_rng, Rng}; - /// - /// let mut rng = task_rng(); - /// let sample = rng.sample(range(1, 100), 5); - /// println!("{:?}", sample); - /// ``` - fn sample<A, T: Iterator<A>>(&mut self, iter: T, n: uint) -> ~[A] { - let mut reservoir : ~[A] = vec::with_capacity(n); - for (i, elem) in iter.enumerate() { - if i < n { - reservoir.push(elem); - continue - } - - let k = self.gen_range(0, i + 1); - if k < reservoir.len() { - reservoir[k] = elem - } - } - reservoir - } -} - -/// A random number generator that can be explicitly seeded to produce -/// the same stream of randomness multiple times. -pub trait SeedableRng<Seed>: Rng { - /// Reseed an RNG with the given seed. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{Rng, SeedableRng, StdRng}; - /// - /// let mut rng: StdRng = SeedableRng::from_seed(&[1, 2, 3, 4]); - /// println!("{}", rng.gen::<f64>()); - /// rng.reseed([5, 6, 7, 8]); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn reseed(&mut self, Seed); - - /// Create a new RNG with the given seed. - /// - /// # Example - /// - /// ```rust - /// use std::rand::{Rng, SeedableRng, StdRng}; - /// - /// let mut rng: StdRng = SeedableRng::from_seed(&[1, 2, 3, 4]); - /// println!("{}", rng.gen::<f64>()); - /// ``` - fn from_seed(seed: Seed) -> Self; -} - -/// Create a random number generator with a default algorithm and seed. -/// -/// It returns the strongest `Rng` algorithm currently implemented in -/// pure Rust. If you require a specifically seeded `Rng` for -/// consistency over time you should pick one algorithm and create the -/// `Rng` yourself. -/// -/// This is a very expensive operation as it has to read randomness -/// from the operating system and use this in an expensive seeding -/// operation. If one does not require high performance generation of -/// random numbers, `task_rng` and/or `random` may be more -/// appropriate. -pub fn rng() -> StdRng { - StdRng::new() -} - -/// The standard RNG. This is designed to be efficient on the current -/// platform. -#[cfg(not(target_word_size="64"))] -pub struct StdRng { priv rng: IsaacRng } - -/// The standard RNG. This is designed to be efficient on the current -/// platform. -#[cfg(target_word_size="64")] -pub struct StdRng { priv rng: Isaac64Rng } - -impl StdRng { - /// Create a randomly seeded instance of `StdRng`. This reads - /// randomness from the OS to seed the PRNG. - #[cfg(not(target_word_size="64"))] - pub fn new() -> StdRng { - StdRng { rng: IsaacRng::new() } - } - /// Create a randomly seeded instance of `StdRng`. This reads - /// randomness from the OS to seed the PRNG. - #[cfg(target_word_size="64")] - pub fn new() -> StdRng { - StdRng { rng: Isaac64Rng::new() } - } -} - -impl Rng for StdRng { - #[inline] - fn next_u32(&mut self) -> u32 { - self.rng.next_u32() - } - - #[inline] - fn next_u64(&mut self) -> u64 { - self.rng.next_u64() - } -} - -impl<'a> SeedableRng<&'a [uint]> for StdRng { - fn reseed(&mut self, seed: &'a [uint]) { - // the internal RNG can just be seeded from the above - // randomness. - self.rng.reseed(unsafe {cast::transmute(seed)}) - } - - fn from_seed(seed: &'a [uint]) -> StdRng { - StdRng { rng: SeedableRng::from_seed(unsafe {cast::transmute(seed)}) } - } -} - -/// Create a weak random number generator with a default algorithm and seed. -/// -/// It returns the fastest `Rng` algorithm currently available in Rust without -/// consideration for cryptography or security. If you require a specifically -/// seeded `Rng` for consistency over time you should pick one algorithm and -/// create the `Rng` yourself. -/// -/// This will read randomness from the operating system to seed the -/// generator. -pub fn weak_rng() -> XorShiftRng { - XorShiftRng::new() -} - -/// An Xorshift[1] random number -/// generator. -/// -/// The Xorshift algorithm is not suitable for cryptographic purposes -/// but is very fast. If you do not know for sure that it fits your -/// requirements, use a more secure one such as `IsaacRng` or `OSRng`. -/// -/// [1]: Marsaglia, George (July 2003). ["Xorshift -/// RNGs"](http://www.jstatsoft.org/v08/i14/paper). *Journal of -/// Statistical Software*. Vol. 8 (Issue 14). -pub struct XorShiftRng { - priv x: u32, - priv y: u32, - priv z: u32, - priv w: u32, -} - -impl Rng for XorShiftRng { - #[inline] - fn next_u32(&mut self) -> u32 { - let x = self.x; - let t = x ^ (x << 11); - self.x = self.y; - self.y = self.z; - self.z = self.w; - let w = self.w; - self.w = w ^ (w >> 19) ^ (t ^ (t >> 8)); - self.w - } -} - -impl SeedableRng<[u32, .. 4]> for XorShiftRng { - /// Reseed an XorShiftRng. This will fail if `seed` is entirely 0. - fn reseed(&mut self, seed: [u32, .. 4]) { - assert!(!seed.iter().all(|&x| x == 0), - "XorShiftRng.reseed called with an all zero seed."); - - self.x = seed[0]; - self.y = seed[1]; - self.z = seed[2]; - self.w = seed[3]; - } - - /// Create a new XorShiftRng. This will fail if `seed` is entirely 0. - fn from_seed(seed: [u32, .. 4]) -> XorShiftRng { - assert!(!seed.iter().all(|&x| x == 0), - "XorShiftRng::from_seed called with an all zero seed."); - - XorShiftRng { - x: seed[0], - y: seed[1], - z: seed[2], - w: seed[3] - } - } -} - -impl XorShiftRng { - /// Create an xor shift random number generator with a random seed. - pub fn new() -> XorShiftRng { - let mut s = [0u8, ..16]; - loop { - let mut r = OSRng::new(); - r.fill_bytes(s); - - if !s.iter().all(|x| *x == 0) { - break; - } - } - let s: [u32, ..4] = unsafe { cast::transmute(s) }; - SeedableRng::from_seed(s) - } -} - -/// Controls how the task-local RNG is reseeded. -struct TaskRngReseeder; - -impl reseeding::Reseeder<StdRng> for TaskRngReseeder { - fn reseed(&mut self, rng: &mut StdRng) { - *rng = StdRng::new(); - } -} -static TASK_RNG_RESEED_THRESHOLD: uint = 32_768; -type TaskRngInner = reseeding::ReseedingRng<StdRng, TaskRngReseeder>; -/// The task-local RNG. -pub struct TaskRng { - // This points into TLS (specifically, it points to the endpoint - // of a ~ stored in TLS, to make it robust against TLS moving - // things internally) and so this struct cannot be legally - // transferred between tasks *and* it's unsafe to deallocate the - // RNG other than when a task is finished. - // - // The use of unsafe code here is OK if the invariants above are - // satisfied; and it allows us to avoid (unnecessarily) using a - // GC'd or RC'd pointer. - priv rng: *mut TaskRngInner, - priv marker: marker::NoSend, -} - -// used to make space in TLS for a random number generator -local_data_key!(TASK_RNG_KEY: ~TaskRngInner) - -/// Retrieve the lazily-initialized task-local random number -/// generator, seeded by the system. Intended to be used in method -/// chaining style, e.g. `task_rng().gen::<int>()`. -/// -/// The RNG provided will reseed itself from the operating system -/// after generating a certain amount of randomness. -/// -/// The internal RNG used is platform and architecture dependent, even -/// if the operating system random number generator is rigged to give -/// the same sequence always. If absolute consistency is required, -/// explicitly select an RNG, e.g. `IsaacRng` or `Isaac64Rng`. -pub fn task_rng() -> TaskRng { - local_data::get_mut(TASK_RNG_KEY, |rng| match rng { - None => { - let mut rng = ~reseeding::ReseedingRng::new(StdRng::new(), - TASK_RNG_RESEED_THRESHOLD, - TaskRngReseeder); - let ptr = &mut *rng as *mut TaskRngInner; - - local_data::set(TASK_RNG_KEY, rng); - - TaskRng { rng: ptr, marker: marker::NoSend } - } - Some(rng) => TaskRng { rng: &mut **rng, marker: marker::NoSend } - }) -} - -impl Rng for TaskRng { - fn next_u32(&mut self) -> u32 { - unsafe { (*self.rng).next_u32() } - } - - fn next_u64(&mut self) -> u64 { - unsafe { (*self.rng).next_u64() } - } - - #[inline] - fn fill_bytes(&mut self, bytes: &mut [u8]) { - unsafe { (*self.rng).fill_bytes(bytes) } - } -} - -/// Generate a random value using the task-local random number -/// generator. -/// -/// # Example -/// -/// ```rust -/// use std::rand::random; -/// -/// if random() { -/// let x = random(); -/// println!("{}", 2u * x); -/// } else { -/// println!("{}", random::<f64>()); -/// } -/// ``` -#[inline] -pub fn random<T: Rand>() -> T { - task_rng().gen() -} - -/// A wrapper for generating floating point numbers uniformly in the -/// open interval `(0,1)` (not including either endpoint). -/// -/// Use `Closed01` for the closed interval `[0,1]`, and the default -/// `Rand` implementation for `f32` and `f64` for the half-open -/// `[0,1)`. -/// -/// # Example -/// ```rust,ignore -/// use std::rand::{random, Open01}; -/// -/// let Open01(val) = random::<Open01<f32>>(); -/// println!("f32 from (0,1): {}", val); -/// ``` -pub struct Open01<F>(F); - -/// A wrapper for generating floating point numbers uniformly in the -/// closed interval `[0,1]` (including both endpoints). -/// -/// Use `Open01` for the closed interval `(0,1)`, and the default -/// `Rand` implementation of `f32` and `f64` for the half-open -/// `[0,1)`. -/// -/// # Example -/// ```rust,ignore -/// use std::rand::{random, Closed01}; -/// -/// let Closed01(val) = random::<Closed01<f32>>(); -/// println!("f32 from [0,1]: {}", val); -/// ``` -pub struct Closed01<F>(F); - -#[cfg(test)] -mod test { - use prelude::*; - use vec; - use super::*; - - struct ConstRng { i: u64 } - impl Rng for ConstRng { - fn next_u32(&mut self) -> u32 { self.i as u32 } - fn next_u64(&mut self) -> u64 { self.i } - - // no fill_bytes on purpose - } - - #[test] - fn test_fill_bytes_default() { - let mut r = ConstRng { i: 0x11_22_33_44_55_66_77_88 }; - - // check every remainder mod 8, both in small and big vectors. - let lengths = [0, 1, 2, 3, 4, 5, 6, 7, - 80, 81, 82, 83, 84, 85, 86, 87]; - for &n in lengths.iter() { - let mut v = vec::from_elem(n, 0u8); - r.fill_bytes(v); - - // use this to get nicer error messages. - for (i, &byte) in v.iter().enumerate() { - if byte == 0 { - fail!("byte {} of {} is zero", i, n) - } - } - } - } - - #[test] - fn test_gen_range() { - let mut r = rng(); - for _ in range(0, 1000) { - let a = r.gen_range(-3i, 42); - assert!(a >= -3 && a < 42); - assert_eq!(r.gen_range(0, 1), 0); - assert_eq!(r.gen_range(-12, -11), -12); - } - - for _ in range(0, 1000) { - let a = r.gen_range(10, 42); - assert!(a >= 10 && a < 42); - assert_eq!(r.gen_range(0, 1), 0); - assert_eq!(r.gen_range(3_000_000u, 3_000_001), 3_000_000); - } - - } - - #[test] - #[should_fail] - fn test_gen_range_fail_int() { - let mut r = rng(); - r.gen_range(5i, -2); - } - - #[test] - #[should_fail] - fn test_gen_range_fail_uint() { - let mut r = rng(); - r.gen_range(5u, 2u); - } - - #[test] - fn test_gen_f64() { - let mut r = rng(); - let a = r.gen::<f64>(); - let b = r.gen::<f64>(); - debug!("{:?}", (a, b)); - } - - #[test] - fn test_gen_weighted_bool() { - let mut r = rng(); - assert_eq!(r.gen_weighted_bool(0u), true); - assert_eq!(r.gen_weighted_bool(1u), true); - } - - #[test] - fn test_gen_ascii_str() { - let mut r = rng(); - debug!("{}", r.gen_ascii_str(10u)); - debug!("{}", r.gen_ascii_str(10u)); - debug!("{}", r.gen_ascii_str(10u)); - assert_eq!(r.gen_ascii_str(0u).len(), 0u); - assert_eq!(r.gen_ascii_str(10u).len(), 10u); - assert_eq!(r.gen_ascii_str(16u).len(), 16u); - } - - #[test] - fn test_gen_vec() { - let mut r = rng(); - assert_eq!(r.gen_vec::<u8>(0u).len(), 0u); - assert_eq!(r.gen_vec::<u8>(10u).len(), 10u); - assert_eq!(r.gen_vec::<f64>(16u).len(), 16u); - } - - #[test] - fn test_choose() { - let mut r = rng(); - assert_eq!(r.choose([1, 1, 1]), 1); - } - - #[test] - fn test_choose_option() { - let mut r = rng(); - let v: &[int] = &[]; - assert!(r.choose_option(v).is_none()); - - let i = 1; - let v = [1,1,1]; - assert_eq!(r.choose_option(v), Some(&i)); - } - - #[test] - fn test_shuffle() { - let mut r = rng(); - let empty: ~[int] = ~[]; - assert_eq!(r.shuffle(~[]), empty); - assert_eq!(r.shuffle(~[1, 1, 1]), ~[1, 1, 1]); - } - - #[test] - fn test_task_rng() { - let mut r = task_rng(); - r.gen::<int>(); - assert_eq!(r.shuffle(~[1, 1, 1]), ~[1, 1, 1]); - assert_eq!(r.gen_range(0u, 1u), 0u); - } - - #[test] - fn test_random() { - // not sure how to test this aside from just getting some values - let _n : uint = random(); - let _f : f32 = random(); - let _o : Option<Option<i8>> = random(); - let _many : ((), - (~uint, @int, ~Option<~(@u32, ~(@bool,))>), - (u8, i8, u16, i16, u32, i32, u64, i64), - (f32, (f64, (f64,)))) = random(); - } - - #[test] - fn test_sample() { - let min_val = 1; - let max_val = 100; - - let mut r = rng(); - let vals = range(min_val, max_val).to_owned_vec(); - let small_sample = r.sample(vals.iter(), 5); - let large_sample = r.sample(vals.iter(), vals.len() + 5); - - assert_eq!(small_sample.len(), 5); - assert_eq!(large_sample.len(), vals.len()); - - assert!(small_sample.iter().all(|e| { - **e >= min_val && **e <= max_val - })); - } - - #[test] - fn test_std_rng_seeded() { - let s = OSRng::new().gen_vec::<uint>(256); - let mut ra: StdRng = SeedableRng::from_seed(s.as_slice()); - let mut rb: StdRng = SeedableRng::from_seed(s.as_slice()); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - - #[test] - fn test_std_rng_reseed() { - let s = OSRng::new().gen_vec::<uint>(256); - let mut r: StdRng = SeedableRng::from_seed(s.as_slice()); - let string1 = r.gen_ascii_str(100); - - r.reseed(s); - - let string2 = r.gen_ascii_str(100); - assert_eq!(string1, string2); - } -} - -#[cfg(test)] -static RAND_BENCH_N: u64 = 100; - -#[cfg(test)] -mod bench { - extern crate test; - use self::test::BenchHarness; - use prelude::*; - use rand::{XorShiftRng, StdRng, IsaacRng, Isaac64Rng, Rng, RAND_BENCH_N}; - use mem::size_of; - - #[bench] - fn rand_xorshift(bh: &mut BenchHarness) { - let mut rng = XorShiftRng::new(); - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - rng.gen::<uint>(); - } - }); - bh.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; - } - - #[bench] - fn rand_isaac(bh: &mut BenchHarness) { - let mut rng = IsaacRng::new(); - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - rng.gen::<uint>(); - } - }); - bh.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; - } - - #[bench] - fn rand_isaac64(bh: &mut BenchHarness) { - let mut rng = Isaac64Rng::new(); - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - rng.gen::<uint>(); - } - }); - bh.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; - } - - #[bench] - fn rand_std(bh: &mut BenchHarness) { - let mut rng = StdRng::new(); - bh.iter(|| { - for _ in range(0, RAND_BENCH_N) { - rng.gen::<uint>(); - } - }); - bh.bytes = size_of::<uint>() as u64 * RAND_BENCH_N; - } - - #[bench] - fn rand_shuffle_100(bh: &mut BenchHarness) { - let mut rng = XorShiftRng::new(); - let x : &mut[uint] = [1,..100]; - bh.iter(|| { - rng.shuffle_mut(x); - }) - } -} diff --git a/src/libstd/rand/os.rs b/src/libstd/rand/os.rs deleted file mode 100644 index e9068c6b0c8..00000000000 --- a/src/libstd/rand/os.rs +++ /dev/null @@ -1,188 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! Interfaces to the operating system provided random number -//! generators. - -use rand::Rng; -use ops::Drop; - -#[cfg(unix)] -use rand::reader::ReaderRng; -#[cfg(unix)] -use io::File; - -#[cfg(windows)] -use cast; -#[cfg(windows)] -use libc::{c_long, DWORD, BYTE}; -#[cfg(windows)] -type HCRYPTPROV = c_long; -// the extern functions imported from the runtime on Windows are -// implemented so that they either succeed or abort(), so we can just -// assume they work when we call them. - -/// A random number generator that retrieves randomness straight from -/// the operating system. Platform sources: -/// -/// - Unix-like systems (Linux, Android, Mac OSX): read directly from -/// `/dev/urandom`. -/// - Windows: calls `CryptGenRandom`, using the default cryptographic -/// service provider with the `PROV_RSA_FULL` type. -/// -/// This does not block. -#[cfg(unix)] -pub struct OSRng { - priv inner: ReaderRng<File> -} -/// A random number generator that retrieves randomness straight from -/// the operating system. Platform sources: -/// -/// - Unix-like systems (Linux, Android, Mac OSX): read directly from -/// `/dev/urandom`. -/// - Windows: calls `CryptGenRandom`, using the default cryptographic -/// service provider with the `PROV_RSA_FULL` type. -/// -/// This does not block. -#[cfg(windows)] -pub struct OSRng { - priv hcryptprov: HCRYPTPROV -} - -impl OSRng { - /// Create a new `OSRng`. - #[cfg(unix)] - pub fn new() -> OSRng { - use path::Path; - let reader = File::open(&Path::new("/dev/urandom")); - let reader = reader.ok().expect("Error opening /dev/urandom"); - let reader_rng = ReaderRng::new(reader); - - OSRng { inner: reader_rng } - } - - /// Create a new `OSRng`. - #[cfg(windows)] - pub fn new() -> OSRng { - extern { fn rust_win32_rand_acquire(phProv: *mut HCRYPTPROV); } - - let mut hcp = 0; - unsafe {rust_win32_rand_acquire(&mut hcp)}; - - OSRng { hcryptprov: hcp } - } -} - -#[cfg(unix)] -impl Rng for OSRng { - fn next_u32(&mut self) -> u32 { - self.inner.next_u32() - } - fn next_u64(&mut self) -> u64 { - self.inner.next_u64() - } - fn fill_bytes(&mut self, v: &mut [u8]) { - self.inner.fill_bytes(v) - } -} - -#[cfg(windows)] -impl Rng for OSRng { - fn next_u32(&mut self) -> u32 { - let mut v = [0u8, .. 4]; - self.fill_bytes(v); - unsafe { cast::transmute(v) } - } - fn next_u64(&mut self) -> u64 { - let mut v = [0u8, .. 8]; - self.fill_bytes(v); - unsafe { cast::transmute(v) } - } - fn fill_bytes(&mut self, v: &mut [u8]) { - use container::Container; - use vec::MutableVector; - - extern { - fn rust_win32_rand_gen(hProv: HCRYPTPROV, dwLen: DWORD, - pbBuffer: *mut BYTE); - } - - unsafe {rust_win32_rand_gen(self.hcryptprov, v.len() as DWORD, v.as_mut_ptr())} - } -} - -impl Drop for OSRng { - #[cfg(unix)] - fn drop(&mut self) { - // ensure that OSRng is not implicitly copyable on all - // platforms, for consistency. - } - - #[cfg(windows)] - fn drop(&mut self) { - extern { fn rust_win32_rand_release(hProv: HCRYPTPROV); } - - unsafe {rust_win32_rand_release(self.hcryptprov)} - } -} - - -#[cfg(test)] -mod test { - use prelude::*; - use super::*; - use rand::Rng; - use task; - - #[test] - fn test_os_rng() { - let mut r = OSRng::new(); - - r.next_u32(); - r.next_u64(); - - let mut v = [0u8, .. 1000]; - r.fill_bytes(v); - } - - #[test] - fn test_os_rng_tasks() { - - let mut chans = ~[]; - for _ in range(0, 20) { - let (p, c) = Chan::new(); - chans.push(c); - task::spawn(proc() { - // wait until all the tasks are ready to go. - p.recv(); - - // deschedule to attempt to interleave things as much - // as possible (XXX: is this a good test?) - let mut r = OSRng::new(); - task::deschedule(); - let mut v = [0u8, .. 1000]; - - for _ in range(0, 100) { - r.next_u32(); - task::deschedule(); - r.next_u64(); - task::deschedule(); - r.fill_bytes(v); - task::deschedule(); - } - }) - } - - // start all the tasks - for c in chans.iter() { - c.send(()) - } - } -} diff --git a/src/libstd/rand/rand_impls.rs b/src/libstd/rand/rand_impls.rs deleted file mode 100644 index 8f4752b3c44..00000000000 --- a/src/libstd/rand/rand_impls.rs +++ /dev/null @@ -1,278 +0,0 @@ -// Copyright 2013-2014 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! The implementations of `Rand` for the built-in types. - -use char; -use int; -use option::{Option, Some, None}; -use rand::{Rand,Rng}; -use uint; - -impl Rand for int { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> int { - if int::BITS == 32 { - rng.gen::<i32>() as int - } else { - rng.gen::<i64>() as int - } - } -} - -impl Rand for i8 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i8 { - rng.next_u32() as i8 - } -} - -impl Rand for i16 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i16 { - rng.next_u32() as i16 - } -} - -impl Rand for i32 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i32 { - rng.next_u32() as i32 - } -} - -impl Rand for i64 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> i64 { - rng.next_u64() as i64 - } -} - -impl Rand for uint { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> uint { - if uint::BITS == 32 { - rng.gen::<u32>() as uint - } else { - rng.gen::<u64>() as uint - } - } -} - -impl Rand for u8 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u8 { - rng.next_u32() as u8 - } -} - -impl Rand for u16 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u16 { - rng.next_u32() as u16 - } -} - -impl Rand for u32 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u32 { - rng.next_u32() - } -} - -impl Rand for u64 { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> u64 { - rng.next_u64() - } -} - -macro_rules! float_impls { - ($mod_name:ident, $ty:ty, $mantissa_bits:expr, $method_name:ident, $ignored_bits:expr) => { - mod $mod_name { - use rand::{Rand, Rng, Open01, Closed01}; - - static SCALE: $ty = (1u64 << $mantissa_bits) as $ty; - - impl Rand for $ty { - /// Generate a floating point number in the half-open - /// interval `[0,1)`. - /// - /// See `Closed01` for the closed interval `[0,1]`, - /// and `Open01` for the open interval `(0,1)`. - #[inline] - fn rand<R: Rng>(rng: &mut R) -> $ty { - // using any more than `mantissa_bits` bits will - // cause (e.g.) 0xffff_ffff to correspond to 1 - // exactly, so we need to drop some (8 for f32, 11 - // for f64) to guarantee the open end. - (rng.$method_name() >> $ignored_bits) as $ty / SCALE - } - } - impl Rand for Open01<$ty> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Open01<$ty> { - // add a small amount (specifically 2 bits below - // the precision of f64/f32 at 1.0), so that small - // numbers are larger than 0, but large numbers - // aren't pushed to/above 1. - Open01(((rng.$method_name() >> $ignored_bits) as $ty + 0.25) / SCALE) - } - } - impl Rand for Closed01<$ty> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Closed01<$ty> { - // divide by the maximum value of the numerator to - // get a non-zero probability of getting exactly - // 1.0. - Closed01((rng.$method_name() >> $ignored_bits) as $ty / (SCALE - 1.0)) - } - } - } - } -} -float_impls! { f64_rand_impls, f64, 53, next_u64, 11 } -float_impls! { f32_rand_impls, f32, 24, next_u32, 8 } - -impl Rand for char { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> char { - // a char is 21 bits - static CHAR_MASK: u32 = 0x001f_ffff; - loop { - // Rejection sampling. About 0.2% of numbers with at most - // 21-bits are invalid codepoints (surrogates), so this - // will succeed first go almost every time. - match char::from_u32(rng.next_u32() & CHAR_MASK) { - Some(c) => return c, - None => {} - } - } - } -} - -impl Rand for bool { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> bool { - rng.gen::<u8>() & 1 == 1 - } -} - -macro_rules! tuple_impl { - // use variables to indicate the arity of the tuple - ($($tyvar:ident),* ) => { - // the trailing commas are for the 1 tuple - impl< - $( $tyvar : Rand ),* - > Rand for ( $( $tyvar ),* , ) { - - #[inline] - fn rand<R: Rng>(_rng: &mut R) -> ( $( $tyvar ),* , ) { - ( - // use the $tyvar's to get the appropriate number of - // repeats (they're not actually needed) - $( - _rng.gen::<$tyvar>() - ),* - , - ) - } - } - } -} - -impl Rand for () { - #[inline] - fn rand<R: Rng>(_: &mut R) -> () { () } -} -tuple_impl!{A} -tuple_impl!{A, B} -tuple_impl!{A, B, C} -tuple_impl!{A, B, C, D} -tuple_impl!{A, B, C, D, E} -tuple_impl!{A, B, C, D, E, F} -tuple_impl!{A, B, C, D, E, F, G} -tuple_impl!{A, B, C, D, E, F, G, H} -tuple_impl!{A, B, C, D, E, F, G, H, I} -tuple_impl!{A, B, C, D, E, F, G, H, I, J} - -impl<T:Rand> Rand for Option<T> { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> Option<T> { - if rng.gen() { - Some(rng.gen()) - } else { - None - } - } -} - -impl<T: Rand> Rand for ~T { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> ~T { ~rng.gen() } -} - -impl<T: Rand + 'static> Rand for @T { - #[inline] - fn rand<R: Rng>(rng: &mut R) -> @T { @rng.gen() } -} - -#[cfg(test)] -mod tests { - use prelude::*; - use rand::{Rng, task_rng, Open01, Closed01}; - - struct ConstantRng(u64); - impl Rng for ConstantRng { - fn next_u32(&mut self) -> u32 { - let ConstantRng(v) = *self; - v as u32 - } - fn next_u64(&mut self) -> u64 { - let ConstantRng(v) = *self; - v - } - } - - #[test] - fn floating_point_edge_cases() { - // the test for exact equality is correct here. - assert!(ConstantRng(0xffff_ffff).gen::<f32>() != 1.0) - assert!(ConstantRng(0xffff_ffff_ffff_ffff).gen::<f64>() != 1.0) - } - - #[test] - fn rand_open() { - // this is unlikely to catch an incorrect implementation that - // generates exactly 0 or 1, but it keeps it sane. - let mut rng = task_rng(); - for _ in range(0, 1_000) { - // strict inequalities - let Open01(f) = rng.gen::<Open01<f64>>(); - assert!(0.0 < f && f < 1.0); - - let Open01(f) = rng.gen::<Open01<f32>>(); - assert!(0.0 < f && f < 1.0); - } - } - - #[test] - fn rand_closed() { - let mut rng = task_rng(); - for _ in range(0, 1_000) { - // strict inequalities - let Closed01(f) = rng.gen::<Closed01<f64>>(); - assert!(0.0 <= f && f <= 1.0); - - let Closed01(f) = rng.gen::<Closed01<f32>>(); - assert!(0.0 <= f && f <= 1.0); - } - } -} diff --git a/src/libstd/rand/reader.rs b/src/libstd/rand/reader.rs deleted file mode 100644 index 4c9a8f7f9a2..00000000000 --- a/src/libstd/rand/reader.rs +++ /dev/null @@ -1,124 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! A wrapper around any Reader to treat it as an RNG. - -use container::Container; -use result::{Ok, Err}; -use io::Reader; - -use rand::Rng; - -/// An RNG that reads random bytes straight from a `Reader`. This will -/// work best with an infinite reader, but this is not required. -/// -/// It will fail if it there is insufficient data to fulfill a request. -/// -/// # Example -/// -/// ```rust -/// use std::rand::{reader, Rng}; -/// use std::io::MemReader; -/// -/// let mut rng = reader::ReaderRng::new(MemReader::new(~[1,2,3,4,5,6,7,8])); -/// println!("{:x}", rng.gen::<uint>()); -/// ``` -pub struct ReaderRng<R> { - priv reader: R -} - -impl<R: Reader> ReaderRng<R> { - /// Create a new `ReaderRng` from a `Reader`. - pub fn new(r: R) -> ReaderRng<R> { - ReaderRng { - reader: r - } - } -} - -impl<R: Reader> Rng for ReaderRng<R> { - fn next_u32(&mut self) -> u32 { - // This is designed for speed: reading a LE integer on a LE - // platform just involves blitting the bytes into the memory - // of the u32, similarly for BE on BE; avoiding byteswapping. - if cfg!(target_endian="little") { - self.reader.read_le_u32().unwrap() - } else { - self.reader.read_be_u32().unwrap() - } - } - fn next_u64(&mut self) -> u64 { - // see above for explanation. - if cfg!(target_endian="little") { - self.reader.read_le_u64().unwrap() - } else { - self.reader.read_be_u64().unwrap() - } - } - fn fill_bytes(&mut self, v: &mut [u8]) { - if v.len() == 0 { return } - match self.reader.read(v) { - Ok(n) if n == v.len() => return, - Ok(n) => fail!("ReaderRng.fill_bytes could not fill buffer: \ - read {} out of {} bytes.", n, v.len()), - Err(e) => fail!("ReaderRng.fill_bytes error: {}", e) - } - } -} - -#[cfg(test)] -mod test { - use super::*; - use io::MemReader; - use cast; - use rand::*; - use prelude::*; - - #[test] - fn test_reader_rng_u64() { - // transmute from the target to avoid endianness concerns. - let v = ~[1u64, 2u64, 3u64]; - let bytes: ~[u8] = unsafe {cast::transmute(v)}; - let mut rng = ReaderRng::new(MemReader::new(bytes)); - - assert_eq!(rng.next_u64(), 1); - assert_eq!(rng.next_u64(), 2); - assert_eq!(rng.next_u64(), 3); - } - #[test] - fn test_reader_rng_u32() { - // transmute from the target to avoid endianness concerns. - let v = ~[1u32, 2u32, 3u32]; - let bytes: ~[u8] = unsafe {cast::transmute(v)}; - let mut rng = ReaderRng::new(MemReader::new(bytes)); - - assert_eq!(rng.next_u32(), 1); - assert_eq!(rng.next_u32(), 2); - assert_eq!(rng.next_u32(), 3); - } - #[test] - fn test_reader_rng_fill_bytes() { - let v = [1u8, 2, 3, 4, 5, 6, 7, 8]; - let mut w = [0u8, .. 8]; - - let mut rng = ReaderRng::new(MemReader::new(v.to_owned())); - rng.fill_bytes(w); - - assert!(v == w); - } - - #[test] - #[should_fail] - fn test_reader_rng_insufficient_bytes() { - let mut rng = ReaderRng::new(MemReader::new(~[])); - let mut v = [0u8, .. 3]; - rng.fill_bytes(v); - } -} diff --git a/src/libstd/rand/reseeding.rs b/src/libstd/rand/reseeding.rs deleted file mode 100644 index a916ce173fb..00000000000 --- a/src/libstd/rand/reseeding.rs +++ /dev/null @@ -1,224 +0,0 @@ -// Copyright 2013 The Rust Project Developers. See the COPYRIGHT -// file at the top-level directory of this distribution and at -// http://rust-lang.org/COPYRIGHT. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your -// option. This file may not be copied, modified, or distributed -// except according to those terms. - -//! A wrapper around another RNG that reseeds it after it -//! generates a certain number of random bytes. - -use container::Container; -use default::Default; -use rand::{Rng, SeedableRng}; - -/// How many bytes of entropy the underling RNG is allowed to generate -/// before it is reseeded. -static DEFAULT_GENERATION_THRESHOLD: uint = 32 * 1024; - -/// A wrapper around any RNG which reseeds the underlying RNG after it -/// has generated a certain number of random bytes. -pub struct ReseedingRng<R, Rsdr> { - priv rng: R, - priv generation_threshold: uint, - priv bytes_generated: uint, - /// Controls the behaviour when reseeding the RNG. - reseeder: Rsdr -} - -impl<R: Rng, Rsdr: Reseeder<R>> ReseedingRng<R, Rsdr> { - /// Create a new `ReseedingRng` with the given parameters. - /// - /// # Arguments - /// - /// * `rng`: the random number generator to use. - /// * `generation_threshold`: the number of bytes of entropy at which to reseed the RNG. - /// * `reseeder`: the reseeding object to use. - pub fn new(rng: R, generation_threshold: uint, reseeder: Rsdr) -> ReseedingRng<R,Rsdr> { - ReseedingRng { - rng: rng, - generation_threshold: generation_threshold, - bytes_generated: 0, - reseeder: reseeder - } - } - - /// Reseed the internal RNG if the number of bytes that have been - /// generated exceed the threshold. - pub fn reseed_if_necessary(&mut self) { - if self.bytes_generated >= self.generation_threshold { - self.reseeder.reseed(&mut self.rng); - self.bytes_generated = 0; - } - } -} - - -impl<R: Rng, Rsdr: Reseeder<R>> Rng for ReseedingRng<R, Rsdr> { - fn next_u32(&mut self) -> u32 { - self.reseed_if_necessary(); - self.bytes_generated += 4; - self.rng.next_u32() - } - - fn next_u64(&mut self) -> u64 { - self.reseed_if_necessary(); - self.bytes_generated += 8; - self.rng.next_u64() - } - - fn fill_bytes(&mut self, dest: &mut [u8]) { - self.reseed_if_necessary(); - self.bytes_generated += dest.len(); - self.rng.fill_bytes(dest) - } -} - -impl<S, R: SeedableRng<S>, Rsdr: Reseeder<R>> - SeedableRng<(Rsdr, S)> for ReseedingRng<R, Rsdr> { - fn reseed(&mut self, (rsdr, seed): (Rsdr, S)) { - self.rng.reseed(seed); - self.reseeder = rsdr; - self.bytes_generated = 0; - } - /// Create a new `ReseedingRng` from the given reseeder and - /// seed. This uses a default value for `generation_threshold`. - fn from_seed((rsdr, seed): (Rsdr, S)) -> ReseedingRng<R, Rsdr> { - ReseedingRng { - rng: SeedableRng::from_seed(seed), - generation_threshold: DEFAULT_GENERATION_THRESHOLD, - bytes_generated: 0, - reseeder: rsdr - } - } -} - -/// Something that can be used to reseed an RNG via `ReseedingRng`. -/// -/// # Example -/// -/// ```rust -/// use std::rand; -/// use std::rand::{Rng, SeedableRng}; -/// use std::rand::reseeding::{Reseeder, ReseedingRng}; -/// -/// struct TickTockReseeder { tick: bool } -/// impl Reseeder<rand::StdRng> for TickTockReseeder { -/// fn reseed(&mut self, rng: &mut rand::StdRng) { -/// let val = if self.tick {0} else {1}; -/// rng.reseed(&[val]); -/// self.tick = !self.tick; -/// } -/// } -/// fn main() { -/// let rsdr = TickTockReseeder { tick: true }; -/// let mut rng = ReseedingRng::new(rand::StdRng::new(), 10, rsdr); -/// -/// // this will repeat, because it gets reseeded very regularly. -/// println!("{}", rng.gen_ascii_str(100)); -/// } -/// -/// ``` -pub trait Reseeder<R> { - /// Reseed the given RNG. - fn reseed(&mut self, rng: &mut R); -} - -/// Reseed an RNG using a `Default` instance. This reseeds by -/// replacing the RNG with the result of a `Default::default` call. -pub struct ReseedWithDefault; - -impl<R: Rng + Default> Reseeder<R> for ReseedWithDefault { - fn reseed(&mut self, rng: &mut R) { - *rng = Default::default(); - } -} -impl Default for ReseedWithDefault { - fn default() -> ReseedWithDefault { ReseedWithDefault } -} - -#[cfg(test)] -mod test { - use prelude::*; - use super::*; - use default::Default; - use rand::{SeedableRng, Rng}; - - struct Counter { - i: u32 - } - - impl Rng for Counter { - fn next_u32(&mut self) -> u32 { - self.i += 1; - // very random - self.i - 1 - } - } - impl Default for Counter { - fn default() -> Counter { - Counter { i: 0 } - } - } - impl SeedableRng<u32> for Counter { - fn reseed(&mut self, seed: u32) { - self.i = seed; - } - fn from_seed(seed: u32) -> Counter { - Counter { i: seed } - } - } - type MyRng = ReseedingRng<Counter, ReseedWithDefault>; - - #[test] - fn test_reseeding() { - let mut rs = ReseedingRng::new(Counter {i:0}, 400, ReseedWithDefault); - - let mut i = 0; - for _ in range(0, 1000) { - assert_eq!(rs.next_u32(), i % 100); - i += 1; - } - } - - #[test] - fn test_rng_seeded() { - let mut ra: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2)); - let mut rb: MyRng = SeedableRng::from_seed((ReseedWithDefault, 2)); - assert_eq!(ra.gen_ascii_str(100u), rb.gen_ascii_str(100u)); - } - - #[test] - fn test_rng_reseed() { - let mut r: MyRng = SeedableRng::from_seed((ReseedWithDefault, 3)); - let string1 = r.gen_ascii_str(100); - - r.reseed((ReseedWithDefault, 3)); - - let string2 = r.gen_ascii_str(100); - assert_eq!(string1, string2); - } - - static fill_bytes_v_len: uint = 13579; - #[test] - fn test_rng_fill_bytes() { - use rand::task_rng; - let mut v = ~[0u8, .. fill_bytes_v_len]; - task_rng().fill_bytes(v); - - // Sanity test: if we've gotten here, `fill_bytes` has not infinitely - // recursed. - assert_eq!(v.len(), fill_bytes_v_len); - - // To test that `fill_bytes` actually did something, check that the - // average of `v` is not 0. - let mut sum = 0.0; - for &x in v.iter() { - sum += x as f64; - } - assert!(sum / v.len() as f64 != 0.0); - } -} |